notebooks/PyCaret_Regression_Tutorial-Frozen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SAd865lNzZpT"
   },
   "source": [
    "#  <span style=\"color:orange\">Predicting Galaxy Metallicity with PyCaret Regression</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yXN8UznszZpc"
   },
   "source": [
    "**Created using: PyCaret 2.2** <br />\n",
    "**Date Updated: Feb 1, 2021**\n",
    "\n",
    "__[Adapted from Regression Tutorial (REG102) - Level Beginner](https://nbviewer.org/github/pycaret/pycaret/blob/master/tutorials/Regression%20Tutorial%20Level%20Beginner%20-%20REG101.ipynb)__\n",
    "\n",
    "# 1.0  Tutorial Objective\n",
    "This tutorial assumes that you are new to PyCaret and looking to get started applying Regression to astronomy data using the `pycaret.regression` module.\n",
    "\n",
    "In this tutorial we will learn:\n",
    "\n",
    "* **Setting up Environment:**  How to setup an experiment in PyCaret and get started with building regression models\n",
    "* **Create Model:**  How to create a model, perform cross validation and evaluate regression metrics\n",
    "* **Tune Model:**  How to automatically tune the hyperparameters of a regression model\n",
    "* **Plot Model:**  How to analyze model performance using various plots\n",
    "* **Finalize Model:** How to finalize the best model at the end of the experiment\n",
    "* **Predict Model:**  How to make prediction on new / unseen data\n",
    "* **Save / Load Model:**  How to save / load a model for future use\n",
    "\n",
    "# 1.1 Installing PyCaret\n",
    "The first step to get started with PyCaret is to install PyCaret. Installation is easy and will only take a few minutes. Follow the instructions below:\n",
    "\n",
    "# Installing PyCaret in Local Jupyter Notebook\n",
    "`pip install --pre pycaret`  <br />\n",
    "\n",
    "# Installing PyCaret on Google Colab or Azure Notebooks\n",
    "`!pip install --pre pycaret`\n",
    "\n",
    "\n",
    "# 1.2 Pre-Requisites\n",
    "- Python 3.6 or greater\n",
    "- PyCaret 2.0 or greater\n",
    "- Internet connection to load data from pycaret's repository\n",
    "- Basic Knowledge of Regression\n",
    "\n",
    "# 1.4 See also:\n",
    "- __[Regression Tutorial (REG102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/tutorials/Regression%20Tutorial%20Level%20Intermediate%20-%20REG102.ipynb)__\n",
    "- __[Regression Tutorial (REG103) - Level Expert](https://github.com/pycaret/pycaret/blob/master/tutorials/Regression%20Tutorial%20Level%20Expert%20-%20REG103.ipynb)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.5 What is PyCaret?\n",
    "\n",
    "I'm an astronomer, not an ML expert, so I want to spend my time on physical problems - not technical challenges during the ML experiment cycle.\n",
    "\n",
    "PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is essentially a user friendly Python wrapper around several machine learning libraries and frameworks, such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and a few more. PyCaret is ideal for:\n",
    "\n",
    "- Experienced ML users who want to increase productivity.\n",
    "- Astronomers and students who are new to ML and prefer a low code solution.\n",
    "\n",
    "The package is under active development and currently includes the following modules:\n",
    "\n",
    "- **Regression**\n",
    "- **Classification**\n",
    "- **Clustering**\n",
    "- **Time Series (beta)**\n",
    "- **Natural Language Processing**\n",
    "- **Anomaly detection**\n",
    "- **Association Rule Mining**\n",
    "\n",
    "__[Learn More about PyCaret](https://pycaret.gitbook.io/docs/)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0.0.rc8\n"
     ]
    }
   ],
   "source": [
    "import pycaret\n",
    "\n",
    "print(pycaret.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HuEUiXXhzZpi"
   },
   "source": [
    "# 2.0 What is Regression?\n",
    "\n",
    "Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable', or 'target') and one or more independent variables (often called 'features', 'predictors', or 'covariates'). The objective of regression in machine learning is to predict continuous values such as sales amount, quantity, temperature etc.\n",
    "\n",
    "__[Learn More about Regression](https://hbr.org/2015/11/a-refresher-on-regression-analysis)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xnEk7n5ZzZpm"
   },
   "source": [
    "# 3.0 Overview of the Regression Module in PyCaret\n",
    "PyCaret's Regression module (`pycaret.regression`) is a supervised machine learning module which is used for predicting continuous values / outcomes using various techniques and algorithms. Regression can be used for predicting values / outcomes such as sales, units sold, temperature or any number which is continuous.\n",
    "\n",
    "PyCaret's regression module has **over 25 algorithms and 10 plots to analyze the performance of models**. Be it hyper-parameter tuning, ensembling or advanced techniques like stacking, PyCaret's regression module has it all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uN95Uqo6zZpq"
   },
   "source": [
    "# 4.0 Dataset for the Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Guj8GFIJzZpu"
   },
   "source": [
    "For this tutorial we will use tablular data for $\\sim$30,000 galaxies to choose, train, and test a model for predicting galaxy gas-phase metalicities. The data contains the photometry in the five *ugriz* bands, as well as value added measurements of stellar mass (GSWLC), star formation rates (GSWLC), and gas-phase metalicities (MPA-JHU). Short descriptions of each column are as follows:\n",
    "\n",
    "- **ID:** Unique identifyer\n",
    "- **Z:** Redshift\n",
    "- **petroMag_*ugriz*:** Petrosian magnitude in band from SDSS DR7\n",
    "- **lgOH_MEDIAN_T04:** Total log(O/H)+12 gas-phase metallicity from MPA-JHU catalogue `Target Column`\n",
    "- **logMstar_S16:** Total stellar mass from GSWLC\n",
    "- **logSFR_SED_S16:** Total star formation rate from GSWLC\n",
    "\n",
    "\n",
    "# Dataset Acknowledgement:\n",
    "The original dataset description can be [found in Brown+2018.](https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.1868B/abstract)\n",
    "It uses the [Sloan Digital Sky Survey DR7](https://www.sdss4.org/dr12/spectro/galaxy_mpajhu/), the GALEX-Sloan-WISE Legacy Catalogue [(Salim+16)](https://arxiv.org/abs/1610.00712), and the MPA-JHU catalogue [(Tremonti+2004)](https://arxiv.org/abs/astro-ph/0405537)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wwUzzm1YzZpz"
   },
   "source": [
    "# 5.0 Getting the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PFCSZ_NKzZp3"
   },
   "source": [
    "You can download the data from github and load it using pandas __[(Learn How)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:26:54.145524Z",
     "start_time": "2022-07-17T04:26:51.048474Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "id": "H6qS5U--zZp7",
    "outputId": "2a11a81c-7e67-425a-a3ef-091d2c9fbd30"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dataset = pd.read_csv(\"../data/sdss_metallicity_data.csv\")\n",
    "\n",
    "# drop rows with missing data\n",
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:26:54.157512Z",
     "start_time": "2022-07-17T04:26:54.149521Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "D5PerU66zZqK",
    "outputId": "2fdd6ab8-7d68-4cc4-81a7-0cb82ed70799"
   },
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Z</th>\n",
       "      <th>petroMag_u</th>\n",
       "      <th>petroMag_g</th>\n",
       "      <th>petroMag_r</th>\n",
       "      <th>petroMag_i</th>\n",
       "      <th>petroMag_z</th>\n",
       "      <th>lgOH_MEDIAN_T04</th>\n",
       "      <th>logMstar_S16</th>\n",
       "      <th>logSFR_SED_S16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29562.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>587324.205500</td>\n",
       "      <td>0.034982</td>\n",
       "      <td>17.211239</td>\n",
       "      <td>15.720272</td>\n",
       "      <td>15.081555</td>\n",
       "      <td>14.754757</td>\n",
       "      <td>14.573281</td>\n",
       "      <td>-54.727458</td>\n",
       "      <td>10.005095</td>\n",
       "      <td>-0.600921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>255148.111727</td>\n",
       "      <td>0.009591</td>\n",
       "      <td>100.914028</td>\n",
       "      <td>100.898828</td>\n",
       "      <td>100.892953</td>\n",
       "      <td>100.890032</td>\n",
       "      <td>100.888806</td>\n",
       "      <td>53.582196</td>\n",
       "      <td>0.534391</td>\n",
       "      <td>0.780612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010068</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-99.900000</td>\n",
       "      <td>8.144000</td>\n",
       "      <td>-4.154000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>412175.500000</td>\n",
       "      <td>0.026562</td>\n",
       "      <td>17.529691</td>\n",
       "      <td>16.018704</td>\n",
       "      <td>15.333058</td>\n",
       "      <td>14.976245</td>\n",
       "      <td>14.756415</td>\n",
       "      <td>-99.900000</td>\n",
       "      <td>9.567000</td>\n",
       "      <td>-0.865000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>639011.500000</td>\n",
       "      <td>0.035140</td>\n",
       "      <td>18.269680</td>\n",
       "      <td>16.854943</td>\n",
       "      <td>16.233967</td>\n",
       "      <td>15.908793</td>\n",
       "      <td>15.712407</td>\n",
       "      <td>-99.900000</td>\n",
       "      <td>9.974000</td>\n",
       "      <td>-0.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>781944.000000</td>\n",
       "      <td>0.043918</td>\n",
       "      <td>18.934386</td>\n",
       "      <td>17.576166</td>\n",
       "      <td>17.000017</td>\n",
       "      <td>16.703108</td>\n",
       "      <td>16.540253</td>\n",
       "      <td>8.774382</td>\n",
       "      <td>10.411000</td>\n",
       "      <td>-0.106000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>925550.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>36.557720</td>\n",
       "      <td>25.049350</td>\n",
       "      <td>20.477276</td>\n",
       "      <td>19.946150</td>\n",
       "      <td>28.524105</td>\n",
       "      <td>9.309999</td>\n",
       "      <td>11.612000</td>\n",
       "      <td>1.461000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                  ID             Z    petroMag_u    petroMag_g    petroMag_r  \\\n",
       "count   29562.000000  29562.000000  29562.000000  29562.000000  29562.000000   \n",
       "mean   587324.205500      0.034982     17.211239     15.720272     15.081555   \n",
       "std    255148.111727      0.009591    100.914028    100.898828    100.892953   \n",
       "min         0.000000      0.010068  -9999.000000  -9999.000000  -9999.000000   \n",
       "25%    412175.500000      0.026562     17.529691     16.018704     15.333058   \n",
       "50%    639011.500000      0.035140     18.269680     16.854943     16.233967   \n",
       "75%    781944.000000      0.043918     18.934386     17.576166     17.000017   \n",
       "max    925550.000000      0.050000     36.557720     25.049350     20.477276   \n",
       "\n",
       "         petroMag_i    petroMag_z  lgOH_MEDIAN_T04  logMstar_S16  \\\n",
       "count  29562.000000  29562.000000     29562.000000  29562.000000   \n",
       "mean      14.754757     14.573281       -54.727458     10.005095   \n",
       "std      100.890032    100.888806        53.582196      0.534391   \n",
       "min    -9999.000000  -9999.000000       -99.900000      8.144000   \n",
       "25%       14.976245     14.756415       -99.900000      9.567000   \n",
       "50%       15.908793     15.712407       -99.900000      9.974000   \n",
       "75%       16.703108     16.540253         8.774382     10.411000   \n",
       "max       19.946150     28.524105         9.309999     11.612000   \n",
       "\n",
       "       logSFR_SED_S16  \n",
       "count    29562.000000  \n",
       "mean        -0.600921  \n",
       "std          0.780612  \n",
       "min         -4.154000  \n",
       "25%         -0.865000  \n",
       "50%         -0.420000  \n",
       "75%         -0.106000  \n",
       "max          1.461000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the data\n",
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7eWmeLvYzZqY"
   },
   "source": [
    "In order to demonstrate the `predict_model()` function on unseen data, we use entries that are missing metallicity estimates from original dataset for predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:26:54.184066Z",
     "start_time": "2022-07-17T04:26:54.160087Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "R4K9F7BXzZqc",
    "outputId": "22b1c4e7-a1e1-48d2-8ddc-907e716d5b53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for Modeling: (12282, 10)\n",
      "Unseen Data For Predictions: (17280, 9)\n"
     ]
    }
   ],
   "source": [
    "data = dataset.loc[dataset[\"lgOH_MEDIAN_T04\"] > 7]\n",
    "data_unseen = dataset.drop(data.index).drop('lgOH_MEDIAN_T04', axis=1)\n",
    "\n",
    "data.reset_index(drop=True, inplace=True)\n",
    "data_unseen.reset_index(drop=True, inplace=True)\n",
    "\n",
    "print(\"Data for Modeling: \" + str(data.shape))\n",
    "print(\"Unseen Data For Predictions: \" + str(data_unseen.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='logMstar_S16', ylabel='lgOH_MEDIAN_T04'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.scatter(x=\"logMstar_S16\", y=\"lgOH_MEDIAN_T04\", c=\"DarkBlue\",s=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Z</th>\n",
       "      <th>petroMag_u</th>\n",
       "      <th>petroMag_g</th>\n",
       "      <th>petroMag_r</th>\n",
       "      <th>petroMag_i</th>\n",
       "      <th>petroMag_z</th>\n",
       "      <th>lgOH_MEDIAN_T04</th>\n",
       "      <th>logMstar_S16</th>\n",
       "      <th>logSFR_SED_S16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12282.000000</td>\n",
       "      <td>12282.000000</td>\n",
       "      <td>12282.000000</td>\n",
       "      <td>12282.000000</td>\n",
       "      <td>12282.000000</td>\n",
       "      <td>12282.000000</td>\n",
       "      <td>12282.000000</td>\n",
       "      <td>12282.000000</td>\n",
       "      <td>12282.000000</td>\n",
       "      <td>12282.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>580629.757531</td>\n",
       "      <td>0.036432</td>\n",
       "      <td>15.812025</td>\n",
       "      <td>14.562378</td>\n",
       "      <td>14.064609</td>\n",
       "      <td>13.790054</td>\n",
       "      <td>13.661872</td>\n",
       "      <td>8.827462</td>\n",
       "      <td>9.753535</td>\n",
       "      <td>-0.243474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>256857.424280</td>\n",
       "      <td>0.008999</td>\n",
       "      <td>156.547959</td>\n",
       "      <td>156.528042</td>\n",
       "      <td>156.520383</td>\n",
       "      <td>156.516172</td>\n",
       "      <td>156.514406</td>\n",
       "      <td>0.204175</td>\n",
       "      <td>0.416899</td>\n",
       "      <td>0.355969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010159</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>7.851740</td>\n",
       "      <td>8.418000</td>\n",
       "      <td>-3.212000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>409178.500000</td>\n",
       "      <td>0.029773</td>\n",
       "      <td>17.676838</td>\n",
       "      <td>16.455387</td>\n",
       "      <td>15.948149</td>\n",
       "      <td>15.668969</td>\n",
       "      <td>15.515812</td>\n",
       "      <td>8.684765</td>\n",
       "      <td>9.429000</td>\n",
       "      <td>-0.479000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>630756.500000</td>\n",
       "      <td>0.037077</td>\n",
       "      <td>18.324352</td>\n",
       "      <td>17.130306</td>\n",
       "      <td>16.643019</td>\n",
       "      <td>16.377245</td>\n",
       "      <td>16.241022</td>\n",
       "      <td>8.833564</td>\n",
       "      <td>9.683000</td>\n",
       "      <td>-0.263000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>784719.500000</td>\n",
       "      <td>0.044596</td>\n",
       "      <td>18.863432</td>\n",
       "      <td>17.684808</td>\n",
       "      <td>17.212861</td>\n",
       "      <td>16.950278</td>\n",
       "      <td>16.828156</td>\n",
       "      <td>8.990748</td>\n",
       "      <td>10.022000</td>\n",
       "      <td>-0.021000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>925473.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>33.267735</td>\n",
       "      <td>21.953460</td>\n",
       "      <td>20.477276</td>\n",
       "      <td>19.946150</td>\n",
       "      <td>28.524105</td>\n",
       "      <td>9.309999</td>\n",
       "      <td>11.239000</td>\n",
       "      <td>1.461000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID             Z    petroMag_u    petroMag_g    petroMag_r  \\\n",
       "count   12282.000000  12282.000000  12282.000000  12282.000000  12282.000000   \n",
       "mean   580629.757531      0.036432     15.812025     14.562378     14.064609   \n",
       "std    256857.424280      0.008999    156.547959    156.528042    156.520383   \n",
       "min         0.000000      0.010159  -9999.000000  -9999.000000  -9999.000000   \n",
       "25%    409178.500000      0.029773     17.676838     16.455387     15.948149   \n",
       "50%    630756.500000      0.037077     18.324352     17.130306     16.643019   \n",
       "75%    784719.500000      0.044596     18.863432     17.684808     17.212861   \n",
       "max    925473.000000      0.050000     33.267735     21.953460     20.477276   \n",
       "\n",
       "         petroMag_i    petroMag_z  lgOH_MEDIAN_T04  logMstar_S16  \\\n",
       "count  12282.000000  12282.000000     12282.000000  12282.000000   \n",
       "mean      13.790054     13.661872         8.827462      9.753535   \n",
       "std      156.516172    156.514406         0.204175      0.416899   \n",
       "min    -9999.000000  -9999.000000         7.851740      8.418000   \n",
       "25%       15.668969     15.515812         8.684765      9.429000   \n",
       "50%       16.377245     16.241022         8.833564      9.683000   \n",
       "75%       16.950278     16.828156         8.990748     10.022000   \n",
       "max       19.946150     28.524105         9.309999     11.239000   \n",
       "\n",
       "       logSFR_SED_S16  \n",
       "count    12282.000000  \n",
       "mean        -0.243474  \n",
       "std          0.355969  \n",
       "min         -3.212000  \n",
       "25%         -0.479000  \n",
       "50%         -0.263000  \n",
       "75%         -0.021000  \n",
       "max          1.461000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DxnJV14BzZqq"
   },
   "source": [
    "# 6.0 Setting up Environment in PyCaret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "15-blMPOzZqw"
   },
   "source": [
    "The `setup()` function initializes the environment in pycaret and creates the transformation pipeline to prepare the data for modeling and deployment. `setup()` must be called before executing any other function in pycaret. It takes two mandatory parameters: a pandas dataframe and the name of the target column. All other parameters are optional and are used to customize the pre-processing pipeline (we will see them in later tutorials).\n",
    "\n",
    "When `setup()` is executed, PyCaret's inference algorithm will automatically infer the data types for all features based on certain properties. The data type should be inferred correctly but this is not always the case. To account for this, PyCaret displays a table containing the features and their inferred data types after `setup()` is executed. If all of the data types are correctly identified `enter` can be pressed to continue or `quit` can be typed to end the expriment. Ensuring that the data types are correct is of fundamental importance in PyCaret as it automatically performs a few pre-processing tasks which are imperative to any machine learning experiment. These tasks are performed differently for each data type which means it is very important for them to be correctly configured.\n",
    "\n",
    "In later tutorials we will learn how to overwrite PyCaret's inferred data type using the `numeric_features` and `categorical_features` parameters in `setup()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:27:09.280979Z",
     "start_time": "2022-07-17T04:26:54.191209Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 803
    },
    "colab_type": "code",
    "id": "7V2FN4KQzZrA",
    "outputId": "43d8d23d-ef08-438a-8cc3-ba78e9773aca"
   },
   "outputs": [
    {
     "data": {
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       "<style type=\"text/css\">\n",
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       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_da4e2\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_da4e2_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_da4e2_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_da4e2_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_da4e2_row0_col1\" class=\"data row0 col1\" >3434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_da4e2_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_da4e2_row1_col1\" class=\"data row1 col1\" >lgOH_MEDIAN_T04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_da4e2_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_da4e2_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_da4e2_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
       "      <td id=\"T_da4e2_row3_col1\" class=\"data row3 col1\" >(12282, 10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_da4e2_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_da4e2_row4_col1\" class=\"data row4 col1\" >(12282, 9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_da4e2_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_da4e2_row5_col1\" class=\"data row5 col1\" >(8597, 9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_da4e2_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_da4e2_row6_col1\" class=\"data row6 col1\" >(3685, 9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_da4e2_row7_col0\" class=\"data row7 col0\" >Ignore features</td>\n",
       "      <td id=\"T_da4e2_row7_col1\" class=\"data row7 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_da4e2_row8_col0\" class=\"data row8 col0\" >Numeric features</td>\n",
       "      <td id=\"T_da4e2_row8_col1\" class=\"data row8 col1\" >8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_da4e2_row9_col0\" class=\"data row9 col0\" >Preprocess</td>\n",
       "      <td id=\"T_da4e2_row9_col1\" class=\"data row9 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_da4e2_row10_col0\" class=\"data row10 col0\" >Imputation type</td>\n",
       "      <td id=\"T_da4e2_row10_col1\" class=\"data row10 col1\" >iterative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_da4e2_row11_col0\" class=\"data row11 col0\" >Iterative imputation iterations</td>\n",
       "      <td id=\"T_da4e2_row11_col1\" class=\"data row11 col1\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_da4e2_row12_col0\" class=\"data row12 col0\" >Numeric iterative imputer</td>\n",
       "      <td id=\"T_da4e2_row12_col1\" class=\"data row12 col1\" >lightgbm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_da4e2_row13_col0\" class=\"data row13 col0\" >Categorical iterative imputer</td>\n",
       "      <td id=\"T_da4e2_row13_col1\" class=\"data row13 col1\" >lightgbm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_da4e2_row14_col0\" class=\"data row14 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_da4e2_row14_col1\" class=\"data row14 col1\" >KFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_da4e2_row15_col0\" class=\"data row15 col0\" >Fold Number</td>\n",
       "      <td id=\"T_da4e2_row15_col1\" class=\"data row15 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_da4e2_row16_col0\" class=\"data row16 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_da4e2_row16_col1\" class=\"data row16 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_da4e2_row17_col0\" class=\"data row17 col0\" >Use GPU</td>\n",
       "      <td id=\"T_da4e2_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_da4e2_row18_col0\" class=\"data row18 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_da4e2_row18_col1\" class=\"data row18 col1\" >MlflowLogger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_da4e2_row19_col0\" class=\"data row19 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_da4e2_row19_col1\" class=\"data row19 col1\" >galaxy_metallicity_log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_da4e2_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_da4e2_row20_col0\" class=\"data row20 col0\" >USI</td>\n",
       "      <td id=\"T_da4e2_row20_col1\" class=\"data row20 col1\" >3961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9f2cc572b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.regression import *\n",
    "\n",
    "exp_reg101 = setup(\n",
    "    data=data,\n",
    "    target=\"lgOH_MEDIAN_T04\",  # target column\n",
    "    log_experiment=True,\n",
    "    experiment_name=\"galaxy_metallicity_log\",\n",
    "    imputation_type=\"iterative\",\n",
    "    ignore_features=[\"ID\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nWBypX32zZrP"
   },
   "source": [
    "Once the setup has been successfully executed it prints the information grid which contains several important pieces of information. Most of the information is related to the pre-processing pipeline which is constructed when `setup()` is executed. The majority of these features are out of scope for the purposes of this tutorial. However, a few important things to note at this stage include:\n",
    "\n",
    "- **session_id :**  A pseudo-random number distributed as a seed in all functions for later reproducibility. If no `session_id` is passed, a random number is automatically generated that is distributed to all functions. In this experiment, the `session_id` is set as `123` for later reproducibility.<br/>\n",
    "<br/>\n",
    "- **Original Data :**  Displays the original shape of dataset. In this experiment (5400, 8) means 5400 samples and 8 features including the target column. <br/>\n",
    "<br/>\n",
    "- **Missing Values :**  When there are missing values in the original data, this will show as True. For this experiment there are no missing values in the dataset.<br/>\n",
    "<br/>\n",
    "- **Numeric Features :**  Number of features inferred as numeric. In this dataset, 1 out of 8 features are inferred as numeric. <br/>\n",
    "<br/>\n",
    "- **Categorical Features :**  Number of features inferred as categorical. In this dataset, 6 out of 8 features are inferred as categorical. <br/>\n",
    "<br/>\n",
    "- **Transformed Train Set :** Displays the shape of the transformed training set. Notice that the original shape of (5400, 8) is transformed into (3779, 28) for the transformed train set. The number of features has increased from 8 from 28 due to categorical encoding <br/>\n",
    "<br/>\n",
    "- **Transformed Test Set :** Displays the shape of transformed test/hold-out set. There are 1621 samples in test/hold-out set. This split is based on the default value of 70/30 that can be changed using `train_size` parameter in setup. <br/>\n",
    "\n",
    "Notice how a few tasks that are imperative to perform modeling are automatically handled, such as missing value imputation (in this case there are no missing values in training data, but we still need imputers for unseen data), categorical encoding etc. Most of the parameters in `setup()` are optional and used for customizing the pre-processing pipeline. These parameters are out of scope for this tutorial but as you progress to the intermediate and expert levels, we will cover them in much greater detail.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xBqHzabEzZrT"
   },
   "source": [
    "# 7.0 Comparing All Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QHiNl6UmzZrW"
   },
   "source": [
    "Comparing all models to evaluate performance is the recommended starting point for modeling once the setup is completed (unless you exactly know what kind of model you need, which is often not the case). This function trains all models in the model library and scores them using k-fold cross validation for metric evaluation. The output prints a score grid that shows average MAE, MSE, RMSE, R2, RMSLE and MAPE accross the folds (10 by default) along with training time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:27:47.787311Z",
     "start_time": "2022-07-17T04:27:09.283976Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "atJfMGD6zZrb",
    "outputId": "af936da0-cc93-4429-ef61-20d940e0aa4e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
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       "  text-align: left;\n",
       "}\n",
       "#T_fdaa0_row0_col1, #T_fdaa0_row0_col2, #T_fdaa0_row0_col3, #T_fdaa0_row0_col4, #T_fdaa0_row0_col5, #T_fdaa0_row0_col6 {\n",
       "  text-align: left;\n",
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       "}\n",
       "#T_fdaa0_row0_col7, #T_fdaa0_row1_col7, #T_fdaa0_row2_col7, #T_fdaa0_row3_col7, #T_fdaa0_row4_col7, #T_fdaa0_row5_col7, #T_fdaa0_row6_col7, #T_fdaa0_row7_col7, #T_fdaa0_row8_col7, #T_fdaa0_row9_col7, #T_fdaa0_row10_col7, #T_fdaa0_row12_col7, #T_fdaa0_row13_col7, #T_fdaa0_row14_col7, #T_fdaa0_row15_col7, #T_fdaa0_row16_col7, #T_fdaa0_row17_col7, #T_fdaa0_row19_col7 {\n",
       "  text-align: left;\n",
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       "}\n",
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       "}\n",
       "</style>\n",
       "<table id=\"T_fdaa0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_fdaa0_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_fdaa0_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_fdaa0_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_fdaa0_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_fdaa0_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_fdaa0_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_fdaa0_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "      <th id=\"T_fdaa0_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row0\" class=\"row_heading level0 row0\" >catboost</th>\n",
       "      <td id=\"T_fdaa0_row0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "      <td id=\"T_fdaa0_row0_col1\" class=\"data row0 col1\" >0.0906</td>\n",
       "      <td id=\"T_fdaa0_row0_col2\" class=\"data row0 col2\" >0.0141</td>\n",
       "      <td id=\"T_fdaa0_row0_col3\" class=\"data row0 col3\" >0.1187</td>\n",
       "      <td id=\"T_fdaa0_row0_col4\" class=\"data row0 col4\" >0.6635</td>\n",
       "      <td id=\"T_fdaa0_row0_col5\" class=\"data row0 col5\" >0.0122</td>\n",
       "      <td id=\"T_fdaa0_row0_col6\" class=\"data row0 col6\" >0.0103</td>\n",
       "      <td id=\"T_fdaa0_row0_col7\" class=\"data row0 col7\" >1.9730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row1\" class=\"row_heading level0 row1\" >lightgbm</th>\n",
       "      <td id=\"T_fdaa0_row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_fdaa0_row1_col1\" class=\"data row1 col1\" >0.0912</td>\n",
       "      <td id=\"T_fdaa0_row1_col2\" class=\"data row1 col2\" >0.0143</td>\n",
       "      <td id=\"T_fdaa0_row1_col3\" class=\"data row1 col3\" >0.1195</td>\n",
       "      <td id=\"T_fdaa0_row1_col4\" class=\"data row1 col4\" >0.6588</td>\n",
       "      <td id=\"T_fdaa0_row1_col5\" class=\"data row1 col5\" >0.0123</td>\n",
       "      <td id=\"T_fdaa0_row1_col6\" class=\"data row1 col6\" >0.0104</td>\n",
       "      <td id=\"T_fdaa0_row1_col7\" class=\"data row1 col7\" >0.1330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row2\" class=\"row_heading level0 row2\" >gbr</th>\n",
       "      <td id=\"T_fdaa0_row2_col0\" class=\"data row2 col0\" >Gradient Boosting Regressor</td>\n",
       "      <td id=\"T_fdaa0_row2_col1\" class=\"data row2 col1\" >0.0925</td>\n",
       "      <td id=\"T_fdaa0_row2_col2\" class=\"data row2 col2\" >0.0146</td>\n",
       "      <td id=\"T_fdaa0_row2_col3\" class=\"data row2 col3\" >0.1207</td>\n",
       "      <td id=\"T_fdaa0_row2_col4\" class=\"data row2 col4\" >0.6517</td>\n",
       "      <td id=\"T_fdaa0_row2_col5\" class=\"data row2 col5\" >0.0124</td>\n",
       "      <td id=\"T_fdaa0_row2_col6\" class=\"data row2 col6\" >0.0105</td>\n",
       "      <td id=\"T_fdaa0_row2_col7\" class=\"data row2 col7\" >1.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
       "      <td id=\"T_fdaa0_row3_col0\" class=\"data row3 col0\" >Random Forest Regressor</td>\n",
       "      <td id=\"T_fdaa0_row3_col1\" class=\"data row3 col1\" >0.0931</td>\n",
       "      <td id=\"T_fdaa0_row3_col2\" class=\"data row3 col2\" >0.0148</td>\n",
       "      <td id=\"T_fdaa0_row3_col3\" class=\"data row3 col3\" >0.1217</td>\n",
       "      <td id=\"T_fdaa0_row3_col4\" class=\"data row3 col4\" >0.6463</td>\n",
       "      <td id=\"T_fdaa0_row3_col5\" class=\"data row3 col5\" >0.0125</td>\n",
       "      <td id=\"T_fdaa0_row3_col6\" class=\"data row3 col6\" >0.0106</td>\n",
       "      <td id=\"T_fdaa0_row3_col7\" class=\"data row3 col7\" >2.4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row4\" class=\"row_heading level0 row4\" >et</th>\n",
       "      <td id=\"T_fdaa0_row4_col0\" class=\"data row4 col0\" >Extra Trees Regressor</td>\n",
       "      <td id=\"T_fdaa0_row4_col1\" class=\"data row4 col1\" >0.0942</td>\n",
       "      <td id=\"T_fdaa0_row4_col2\" class=\"data row4 col2\" >0.0151</td>\n",
       "      <td id=\"T_fdaa0_row4_col3\" class=\"data row4 col3\" >0.1230</td>\n",
       "      <td id=\"T_fdaa0_row4_col4\" class=\"data row4 col4\" >0.6385</td>\n",
       "      <td id=\"T_fdaa0_row4_col5\" class=\"data row4 col5\" >0.0126</td>\n",
       "      <td id=\"T_fdaa0_row4_col6\" class=\"data row4 col6\" >0.0107</td>\n",
       "      <td id=\"T_fdaa0_row4_col7\" class=\"data row4 col7\" >0.9620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row5\" class=\"row_heading level0 row5\" >xgboost</th>\n",
       "      <td id=\"T_fdaa0_row5_col0\" class=\"data row5 col0\" >Extreme Gradient Boosting</td>\n",
       "      <td id=\"T_fdaa0_row5_col1\" class=\"data row5 col1\" >0.0946</td>\n",
       "      <td id=\"T_fdaa0_row5_col2\" class=\"data row5 col2\" >0.0154</td>\n",
       "      <td id=\"T_fdaa0_row5_col3\" class=\"data row5 col3\" >0.1239</td>\n",
       "      <td id=\"T_fdaa0_row5_col4\" class=\"data row5 col4\" >0.6332</td>\n",
       "      <td id=\"T_fdaa0_row5_col5\" class=\"data row5 col5\" >0.0127</td>\n",
       "      <td id=\"T_fdaa0_row5_col6\" class=\"data row5 col6\" >0.0108</td>\n",
       "      <td id=\"T_fdaa0_row5_col7\" class=\"data row5 col7\" >0.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row6\" class=\"row_heading level0 row6\" >br</th>\n",
       "      <td id=\"T_fdaa0_row6_col0\" class=\"data row6 col0\" >Bayesian Ridge</td>\n",
       "      <td id=\"T_fdaa0_row6_col1\" class=\"data row6 col1\" >0.0969</td>\n",
       "      <td id=\"T_fdaa0_row6_col2\" class=\"data row6 col2\" >0.0157</td>\n",
       "      <td id=\"T_fdaa0_row6_col3\" class=\"data row6 col3\" >0.1252</td>\n",
       "      <td id=\"T_fdaa0_row6_col4\" class=\"data row6 col4\" >0.6256</td>\n",
       "      <td id=\"T_fdaa0_row6_col5\" class=\"data row6 col5\" >0.0128</td>\n",
       "      <td id=\"T_fdaa0_row6_col6\" class=\"data row6 col6\" >0.0110</td>\n",
       "      <td id=\"T_fdaa0_row6_col7\" class=\"data row6 col7\" >0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row7\" class=\"row_heading level0 row7\" >lr</th>\n",
       "      <td id=\"T_fdaa0_row7_col0\" class=\"data row7 col0\" >Linear Regression</td>\n",
       "      <td id=\"T_fdaa0_row7_col1\" class=\"data row7 col1\" >0.0969</td>\n",
       "      <td id=\"T_fdaa0_row7_col2\" class=\"data row7 col2\" >0.0157</td>\n",
       "      <td id=\"T_fdaa0_row7_col3\" class=\"data row7 col3\" >0.1252</td>\n",
       "      <td id=\"T_fdaa0_row7_col4\" class=\"data row7 col4\" >0.6256</td>\n",
       "      <td id=\"T_fdaa0_row7_col5\" class=\"data row7 col5\" >0.0128</td>\n",
       "      <td id=\"T_fdaa0_row7_col6\" class=\"data row7 col6\" >0.0110</td>\n",
       "      <td id=\"T_fdaa0_row7_col7\" class=\"data row7 col7\" >0.5030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row8\" class=\"row_heading level0 row8\" >knn</th>\n",
       "      <td id=\"T_fdaa0_row8_col0\" class=\"data row8 col0\" >K Neighbors Regressor</td>\n",
       "      <td id=\"T_fdaa0_row8_col1\" class=\"data row8 col1\" >0.0970</td>\n",
       "      <td id=\"T_fdaa0_row8_col2\" class=\"data row8 col2\" >0.0162</td>\n",
       "      <td id=\"T_fdaa0_row8_col3\" class=\"data row8 col3\" >0.1272</td>\n",
       "      <td id=\"T_fdaa0_row8_col4\" class=\"data row8 col4\" >0.6134</td>\n",
       "      <td id=\"T_fdaa0_row8_col5\" class=\"data row8 col5\" >0.0130</td>\n",
       "      <td id=\"T_fdaa0_row8_col6\" class=\"data row8 col6\" >0.0111</td>\n",
       "      <td id=\"T_fdaa0_row8_col7\" class=\"data row8 col7\" >0.0390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row9\" class=\"row_heading level0 row9\" >lar</th>\n",
       "      <td id=\"T_fdaa0_row9_col0\" class=\"data row9 col0\" >Least Angle Regression</td>\n",
       "      <td id=\"T_fdaa0_row9_col1\" class=\"data row9 col1\" >0.0982</td>\n",
       "      <td id=\"T_fdaa0_row9_col2\" class=\"data row9 col2\" >0.0163</td>\n",
       "      <td id=\"T_fdaa0_row9_col3\" class=\"data row9 col3\" >0.1276</td>\n",
       "      <td id=\"T_fdaa0_row9_col4\" class=\"data row9 col4\" >0.6104</td>\n",
       "      <td id=\"T_fdaa0_row9_col5\" class=\"data row9 col5\" >0.0131</td>\n",
       "      <td id=\"T_fdaa0_row9_col6\" class=\"data row9 col6\" >0.0112</td>\n",
       "      <td id=\"T_fdaa0_row9_col7\" class=\"data row9 col7\" >0.0260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row10\" class=\"row_heading level0 row10\" >ridge</th>\n",
       "      <td id=\"T_fdaa0_row10_col0\" class=\"data row10 col0\" >Ridge Regression</td>\n",
       "      <td id=\"T_fdaa0_row10_col1\" class=\"data row10 col1\" >0.1007</td>\n",
       "      <td id=\"T_fdaa0_row10_col2\" class=\"data row10 col2\" >0.0173</td>\n",
       "      <td id=\"T_fdaa0_row10_col3\" class=\"data row10 col3\" >0.1311</td>\n",
       "      <td id=\"T_fdaa0_row10_col4\" class=\"data row10 col4\" >0.5873</td>\n",
       "      <td id=\"T_fdaa0_row10_col5\" class=\"data row10 col5\" >0.0134</td>\n",
       "      <td id=\"T_fdaa0_row10_col6\" class=\"data row10 col6\" >0.0115</td>\n",
       "      <td id=\"T_fdaa0_row10_col7\" class=\"data row10 col7\" >0.0260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row11\" class=\"row_heading level0 row11\" >omp</th>\n",
       "      <td id=\"T_fdaa0_row11_col0\" class=\"data row11 col0\" >Orthogonal Matching Pursuit</td>\n",
       "      <td id=\"T_fdaa0_row11_col1\" class=\"data row11 col1\" >0.1037</td>\n",
       "      <td id=\"T_fdaa0_row11_col2\" class=\"data row11 col2\" >0.0181</td>\n",
       "      <td id=\"T_fdaa0_row11_col3\" class=\"data row11 col3\" >0.1346</td>\n",
       "      <td id=\"T_fdaa0_row11_col4\" class=\"data row11 col4\" >0.5672</td>\n",
       "      <td id=\"T_fdaa0_row11_col5\" class=\"data row11 col5\" >0.0138</td>\n",
       "      <td id=\"T_fdaa0_row11_col6\" class=\"data row11 col6\" >0.0118</td>\n",
       "      <td id=\"T_fdaa0_row11_col7\" class=\"data row11 col7\" >0.0210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row12\" class=\"row_heading level0 row12\" >ada</th>\n",
       "      <td id=\"T_fdaa0_row12_col0\" class=\"data row12 col0\" >AdaBoost Regressor</td>\n",
       "      <td id=\"T_fdaa0_row12_col1\" class=\"data row12 col1\" >0.1104</td>\n",
       "      <td id=\"T_fdaa0_row12_col2\" class=\"data row12 col2\" >0.0188</td>\n",
       "      <td id=\"T_fdaa0_row12_col3\" class=\"data row12 col3\" >0.1369</td>\n",
       "      <td id=\"T_fdaa0_row12_col4\" class=\"data row12 col4\" >0.5522</td>\n",
       "      <td id=\"T_fdaa0_row12_col5\" class=\"data row12 col5\" >0.0140</td>\n",
       "      <td id=\"T_fdaa0_row12_col6\" class=\"data row12 col6\" >0.0125</td>\n",
       "      <td id=\"T_fdaa0_row12_col7\" class=\"data row12 col7\" >0.3890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row13\" class=\"row_heading level0 row13\" >dt</th>\n",
       "      <td id=\"T_fdaa0_row13_col0\" class=\"data row13 col0\" >Decision Tree Regressor</td>\n",
       "      <td id=\"T_fdaa0_row13_col1\" class=\"data row13 col1\" >0.1297</td>\n",
       "      <td id=\"T_fdaa0_row13_col2\" class=\"data row13 col2\" >0.0289</td>\n",
       "      <td id=\"T_fdaa0_row13_col3\" class=\"data row13 col3\" >0.1699</td>\n",
       "      <td id=\"T_fdaa0_row13_col4\" class=\"data row13 col4\" >0.3099</td>\n",
       "      <td id=\"T_fdaa0_row13_col5\" class=\"data row13 col5\" >0.0174</td>\n",
       "      <td id=\"T_fdaa0_row13_col6\" class=\"data row13 col6\" >0.0148</td>\n",
       "      <td id=\"T_fdaa0_row13_col7\" class=\"data row13 col7\" >0.0590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row14\" class=\"row_heading level0 row14\" >huber</th>\n",
       "      <td id=\"T_fdaa0_row14_col0\" class=\"data row14 col0\" >Huber Regressor</td>\n",
       "      <td id=\"T_fdaa0_row14_col1\" class=\"data row14 col1\" >0.1337</td>\n",
       "      <td id=\"T_fdaa0_row14_col2\" class=\"data row14 col2\" >0.0331</td>\n",
       "      <td id=\"T_fdaa0_row14_col3\" class=\"data row14 col3\" >0.1815</td>\n",
       "      <td id=\"T_fdaa0_row14_col4\" class=\"data row14 col4\" >0.2083</td>\n",
       "      <td id=\"T_fdaa0_row14_col5\" class=\"data row14 col5\" >0.0188</td>\n",
       "      <td id=\"T_fdaa0_row14_col6\" class=\"data row14 col6\" >0.0152</td>\n",
       "      <td id=\"T_fdaa0_row14_col7\" class=\"data row14 col7\" >0.1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row15\" class=\"row_heading level0 row15\" >lasso</th>\n",
       "      <td id=\"T_fdaa0_row15_col0\" class=\"data row15 col0\" >Lasso Regression</td>\n",
       "      <td id=\"T_fdaa0_row15_col1\" class=\"data row15 col1\" >0.1693</td>\n",
       "      <td id=\"T_fdaa0_row15_col2\" class=\"data row15 col2\" >0.0419</td>\n",
       "      <td id=\"T_fdaa0_row15_col3\" class=\"data row15 col3\" >0.2048</td>\n",
       "      <td id=\"T_fdaa0_row15_col4\" class=\"data row15 col4\" >-0.0012</td>\n",
       "      <td id=\"T_fdaa0_row15_col5\" class=\"data row15 col5\" >0.0209</td>\n",
       "      <td id=\"T_fdaa0_row15_col6\" class=\"data row15 col6\" >0.0192</td>\n",
       "      <td id=\"T_fdaa0_row15_col7\" class=\"data row15 col7\" >0.0250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row16\" class=\"row_heading level0 row16\" >en</th>\n",
       "      <td id=\"T_fdaa0_row16_col0\" class=\"data row16 col0\" >Elastic Net</td>\n",
       "      <td id=\"T_fdaa0_row16_col1\" class=\"data row16 col1\" >0.1693</td>\n",
       "      <td id=\"T_fdaa0_row16_col2\" class=\"data row16 col2\" >0.0419</td>\n",
       "      <td id=\"T_fdaa0_row16_col3\" class=\"data row16 col3\" >0.2048</td>\n",
       "      <td id=\"T_fdaa0_row16_col4\" class=\"data row16 col4\" >-0.0012</td>\n",
       "      <td id=\"T_fdaa0_row16_col5\" class=\"data row16 col5\" >0.0209</td>\n",
       "      <td id=\"T_fdaa0_row16_col6\" class=\"data row16 col6\" >0.0192</td>\n",
       "      <td id=\"T_fdaa0_row16_col7\" class=\"data row16 col7\" >0.0220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row17\" class=\"row_heading level0 row17\" >llar</th>\n",
       "      <td id=\"T_fdaa0_row17_col0\" class=\"data row17 col0\" >Lasso Least Angle Regression</td>\n",
       "      <td id=\"T_fdaa0_row17_col1\" class=\"data row17 col1\" >0.1693</td>\n",
       "      <td id=\"T_fdaa0_row17_col2\" class=\"data row17 col2\" >0.0419</td>\n",
       "      <td id=\"T_fdaa0_row17_col3\" class=\"data row17 col3\" >0.2048</td>\n",
       "      <td id=\"T_fdaa0_row17_col4\" class=\"data row17 col4\" >-0.0012</td>\n",
       "      <td id=\"T_fdaa0_row17_col5\" class=\"data row17 col5\" >0.0209</td>\n",
       "      <td id=\"T_fdaa0_row17_col6\" class=\"data row17 col6\" >0.0192</td>\n",
       "      <td id=\"T_fdaa0_row17_col7\" class=\"data row17 col7\" >0.0250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row18\" class=\"row_heading level0 row18\" >dummy</th>\n",
       "      <td id=\"T_fdaa0_row18_col0\" class=\"data row18 col0\" >Dummy Regressor</td>\n",
       "      <td id=\"T_fdaa0_row18_col1\" class=\"data row18 col1\" >0.1693</td>\n",
       "      <td id=\"T_fdaa0_row18_col2\" class=\"data row18 col2\" >0.0419</td>\n",
       "      <td id=\"T_fdaa0_row18_col3\" class=\"data row18 col3\" >0.2048</td>\n",
       "      <td id=\"T_fdaa0_row18_col4\" class=\"data row18 col4\" >-0.0012</td>\n",
       "      <td id=\"T_fdaa0_row18_col5\" class=\"data row18 col5\" >0.0209</td>\n",
       "      <td id=\"T_fdaa0_row18_col6\" class=\"data row18 col6\" >0.0192</td>\n",
       "      <td id=\"T_fdaa0_row18_col7\" class=\"data row18 col7\" >0.0210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fdaa0_level0_row19\" class=\"row_heading level0 row19\" >par</th>\n",
       "      <td id=\"T_fdaa0_row19_col0\" class=\"data row19 col0\" >Passive Aggressive Regressor</td>\n",
       "      <td id=\"T_fdaa0_row19_col1\" class=\"data row19 col1\" >0.3932</td>\n",
       "      <td id=\"T_fdaa0_row19_col2\" class=\"data row19 col2\" >236.2254</td>\n",
       "      <td id=\"T_fdaa0_row19_col3\" class=\"data row19 col3\" >7.0202</td>\n",
       "      <td id=\"T_fdaa0_row19_col4\" class=\"data row19 col4\" >-5534.5509</td>\n",
       "      <td id=\"T_fdaa0_row19_col5\" class=\"data row19 col5\" >0.0478</td>\n",
       "      <td id=\"T_fdaa0_row19_col6\" class=\"data row19 col6\" >0.0448</td>\n",
       "      <td id=\"T_fdaa0_row19_col7\" class=\"data row19 col7\" >0.0230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9f3f6dba30>"
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   ],
   "source": [
    "best = compare_models(exclude=[\"ransac\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "epD0BEVyzZrr"
   },
   "source": [
    "Two simple words of code ***(not even a line)*** have trained and evaluated over 20 models using cross validation. The score grid printed above highlights the highest performing metric for comparison purposes only. The grid by default is sorted using `R2` (highest to lowest) which can be changed by passing `sort` parameter. For example `compare_models(sort = 'RMSLE')` will sort the grid by RMSLE (lower to higher since lower is better). If you want to change the fold parameter from the default value of `10` to a different value then you can use the `fold` parameter. For example `compare_models(fold = 5)` will compare all models on 5 fold cross validation. Reducing the number of folds will improve the training time. By default, compare_models return the best performing model based on default sort order but can be used to return a list of top N models by using `n_select` parameter. </br>\n",
    "\n",
    "Notice that how `exclude` parameter is used to block certain models (in this case `RANSAC`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZzpBazV1zZrx"
   },
   "source": [
    "# 8.0 Create a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IPqPRp5OzZr1"
   },
   "source": [
    "`create_model` is the most granular function in PyCaret and is often the foundation behind most of the PyCaret functionalities. As the name suggests this function trains and evaluates a model using cross validation that can be set with fold parameter. The output prints a score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold. \n",
    "\n",
    "For the remaining part of this tutorial, we will work with the below models as our candidate models. The selections are for illustration purposes only and do not necessarily mean they are the top performing or ideal for this type of data.\n",
    "\n",
    "- AdaBoost Regressor ('ada')\n",
    "- Light Gradient Boosting Machine ('lightgbm') \n",
    "- Decision Tree\t ('dt')\n",
    "\n",
    "There are 25 regressors available in the model library of PyCaret. To see list of all regressors either check the docstring or use `models` function to see the library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:27:47.993786Z",
     "start_time": "2022-07-17T04:27:47.789347Z"
    }
   },
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Turbo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>sklearn.linear_model._base.LinearRegression</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lasso</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>sklearn.linear_model._coordinate_descent.Lasso</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ridge</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>sklearn.linear_model._ridge.Ridge</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>en</th>\n",
       "      <td>Elastic Net</td>\n",
       "      <td>sklearn.linear_model._coordinate_descent.Elast...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lar</th>\n",
       "      <td>Least Angle Regression</td>\n",
       "      <td>sklearn.linear_model._least_angle.Lars</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>llar</th>\n",
       "      <td>Lasso Least Angle Regression</td>\n",
       "      <td>sklearn.linear_model._least_angle.LassoLars</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omp</th>\n",
       "      <td>Orthogonal Matching Pursuit</td>\n",
       "      <td>sklearn.linear_model._omp.OrthogonalMatchingPu...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>br</th>\n",
       "      <td>Bayesian Ridge</td>\n",
       "      <td>sklearn.linear_model._bayes.BayesianRidge</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ard</th>\n",
       "      <td>Automatic Relevance Determination</td>\n",
       "      <td>sklearn.linear_model._bayes.ARDRegression</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>par</th>\n",
       "      <td>Passive Aggressive Regressor</td>\n",
       "      <td>sklearn.linear_model._passive_aggressive.Passi...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ransac</th>\n",
       "      <td>Random Sample Consensus</td>\n",
       "      <td>sklearn.linear_model._ransac.RANSACRegressor</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tr</th>\n",
       "      <td>TheilSen Regressor</td>\n",
       "      <td>sklearn.linear_model._theil_sen.TheilSenRegressor</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>huber</th>\n",
       "      <td>Huber Regressor</td>\n",
       "      <td>sklearn.linear_model._huber.HuberRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kr</th>\n",
       "      <td>Kernel Ridge</td>\n",
       "      <td>sklearn.kernel_ridge.KernelRidge</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svm</th>\n",
       "      <td>Support Vector Regression</td>\n",
       "      <td>sklearn.svm._classes.SVR</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>K Neighbors Regressor</td>\n",
       "      <td>sklearn.neighbors._regression.KNeighborsRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <td>Decision Tree Regressor</td>\n",
       "      <td>sklearn.tree._classes.DecisionTreeRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>Random Forest Regressor</td>\n",
       "      <td>sklearn.ensemble._forest.RandomForestRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>et</th>\n",
       "      <td>Extra Trees Regressor</td>\n",
       "      <td>sklearn.ensemble._forest.ExtraTreesRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ada</th>\n",
       "      <td>AdaBoost Regressor</td>\n",
       "      <td>sklearn.ensemble._weight_boosting.AdaBoostRegr...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gbr</th>\n",
       "      <td>Gradient Boosting Regressor</td>\n",
       "      <td>sklearn.ensemble._gb.GradientBoostingRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp</th>\n",
       "      <td>MLP Regressor</td>\n",
       "      <td>sklearn.neural_network._multilayer_perceptron....</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>Extreme Gradient Boosting</td>\n",
       "      <td>xgboost.sklearn.XGBRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lightgbm</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>lightgbm.sklearn.LGBMRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>CatBoost Regressor</td>\n",
       "      <td>catboost.core.CatBoostRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>Dummy Regressor</td>\n",
       "      <td>sklearn.dummy.DummyRegressor</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Name  \\\n",
       "ID                                            \n",
       "lr                        Linear Regression   \n",
       "lasso                      Lasso Regression   \n",
       "ridge                      Ridge Regression   \n",
       "en                              Elastic Net   \n",
       "lar                  Least Angle Regression   \n",
       "llar           Lasso Least Angle Regression   \n",
       "omp             Orthogonal Matching Pursuit   \n",
       "br                           Bayesian Ridge   \n",
       "ard       Automatic Relevance Determination   \n",
       "par            Passive Aggressive Regressor   \n",
       "ransac              Random Sample Consensus   \n",
       "tr                       TheilSen Regressor   \n",
       "huber                       Huber Regressor   \n",
       "kr                             Kernel Ridge   \n",
       "svm               Support Vector Regression   \n",
       "knn                   K Neighbors Regressor   \n",
       "dt                  Decision Tree Regressor   \n",
       "rf                  Random Forest Regressor   \n",
       "et                    Extra Trees Regressor   \n",
       "ada                      AdaBoost Regressor   \n",
       "gbr             Gradient Boosting Regressor   \n",
       "mlp                           MLP Regressor   \n",
       "xgboost           Extreme Gradient Boosting   \n",
       "lightgbm    Light Gradient Boosting Machine   \n",
       "catboost                 CatBoost Regressor   \n",
       "dummy                       Dummy Regressor   \n",
       "\n",
       "                                                  Reference  Turbo  \n",
       "ID                                                                  \n",
       "lr              sklearn.linear_model._base.LinearRegression   True  \n",
       "lasso        sklearn.linear_model._coordinate_descent.Lasso   True  \n",
       "ridge                     sklearn.linear_model._ridge.Ridge   True  \n",
       "en        sklearn.linear_model._coordinate_descent.Elast...   True  \n",
       "lar                  sklearn.linear_model._least_angle.Lars   True  \n",
       "llar            sklearn.linear_model._least_angle.LassoLars   True  \n",
       "omp       sklearn.linear_model._omp.OrthogonalMatchingPu...   True  \n",
       "br                sklearn.linear_model._bayes.BayesianRidge   True  \n",
       "ard               sklearn.linear_model._bayes.ARDRegression  False  \n",
       "par       sklearn.linear_model._passive_aggressive.Passi...   True  \n",
       "ransac         sklearn.linear_model._ransac.RANSACRegressor  False  \n",
       "tr        sklearn.linear_model._theil_sen.TheilSenRegressor  False  \n",
       "huber            sklearn.linear_model._huber.HuberRegressor   True  \n",
       "kr                         sklearn.kernel_ridge.KernelRidge  False  \n",
       "svm                                sklearn.svm._classes.SVR  False  \n",
       "knn       sklearn.neighbors._regression.KNeighborsRegressor   True  \n",
       "dt              sklearn.tree._classes.DecisionTreeRegressor   True  \n",
       "rf           sklearn.ensemble._forest.RandomForestRegressor   True  \n",
       "et             sklearn.ensemble._forest.ExtraTreesRegressor   True  \n",
       "ada       sklearn.ensemble._weight_boosting.AdaBoostRegr...   True  \n",
       "gbr          sklearn.ensemble._gb.GradientBoostingRegressor   True  \n",
       "mlp       sklearn.neural_network._multilayer_perceptron....  False  \n",
       "xgboost                        xgboost.sklearn.XGBRegressor   True  \n",
       "lightgbm                     lightgbm.sklearn.LGBMRegressor   True  \n",
       "catboost                    catboost.core.CatBoostRegressor   True  \n",
       "dummy                          sklearn.dummy.DummyRegressor   True  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wxKHHQcbzZr5"
   },
   "source": [
    "# 8.1 CatBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:27:48.918242Z",
     "start_time": "2022-07-17T04:27:47.996768Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "-NVGDCR3zZr8",
    "outputId": "06f5fc68-d2a5-4b59-fea2-3661ea1cb29d"
   },
   "outputs": [
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_83b96_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_83b96_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_83b96_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_83b96_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_83b96_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_83b96_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_83b96_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_83b96_row0_col0\" class=\"data row0 col0\" >0.0877</td>\n",
       "      <td id=\"T_83b96_row0_col1\" class=\"data row0 col1\" >0.0135</td>\n",
       "      <td id=\"T_83b96_row0_col2\" class=\"data row0 col2\" >0.1163</td>\n",
       "      <td id=\"T_83b96_row0_col3\" class=\"data row0 col3\" >0.6688</td>\n",
       "      <td id=\"T_83b96_row0_col4\" class=\"data row0 col4\" >0.0120</td>\n",
       "      <td id=\"T_83b96_row0_col5\" class=\"data row0 col5\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_83b96_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_83b96_row1_col0\" class=\"data row1 col0\" >0.0934</td>\n",
       "      <td id=\"T_83b96_row1_col1\" class=\"data row1 col1\" >0.0150</td>\n",
       "      <td id=\"T_83b96_row1_col2\" class=\"data row1 col2\" >0.1226</td>\n",
       "      <td id=\"T_83b96_row1_col3\" class=\"data row1 col3\" >0.6631</td>\n",
       "      <td id=\"T_83b96_row1_col4\" class=\"data row1 col4\" >0.0126</td>\n",
       "      <td id=\"T_83b96_row1_col5\" class=\"data row1 col5\" >0.0107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_83b96_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_83b96_row2_col0\" class=\"data row2 col0\" >0.0882</td>\n",
       "      <td id=\"T_83b96_row2_col1\" class=\"data row2 col1\" >0.0133</td>\n",
       "      <td id=\"T_83b96_row2_col2\" class=\"data row2 col2\" >0.1152</td>\n",
       "      <td id=\"T_83b96_row2_col3\" class=\"data row2 col3\" >0.6835</td>\n",
       "      <td id=\"T_83b96_row2_col4\" class=\"data row2 col4\" >0.0118</td>\n",
       "      <td id=\"T_83b96_row2_col5\" class=\"data row2 col5\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_83b96_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_83b96_row3_col0\" class=\"data row3 col0\" >0.0932</td>\n",
       "      <td id=\"T_83b96_row3_col1\" class=\"data row3 col1\" >0.0142</td>\n",
       "      <td id=\"T_83b96_row3_col2\" class=\"data row3 col2\" >0.1193</td>\n",
       "      <td id=\"T_83b96_row3_col3\" class=\"data row3 col3\" >0.6580</td>\n",
       "      <td id=\"T_83b96_row3_col4\" class=\"data row3 col4\" >0.0122</td>\n",
       "      <td id=\"T_83b96_row3_col5\" class=\"data row3 col5\" >0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_83b96_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_83b96_row4_col0\" class=\"data row4 col0\" >0.0914</td>\n",
       "      <td id=\"T_83b96_row4_col1\" class=\"data row4 col1\" >0.0145</td>\n",
       "      <td id=\"T_83b96_row4_col2\" class=\"data row4 col2\" >0.1205</td>\n",
       "      <td id=\"T_83b96_row4_col3\" class=\"data row4 col3\" >0.6452</td>\n",
       "      <td id=\"T_83b96_row4_col4\" class=\"data row4 col4\" >0.0123</td>\n",
       "      <td id=\"T_83b96_row4_col5\" class=\"data row4 col5\" >0.0104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_83b96_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_83b96_row5_col0\" class=\"data row5 col0\" >0.0915</td>\n",
       "      <td id=\"T_83b96_row5_col1\" class=\"data row5 col1\" >0.0143</td>\n",
       "      <td id=\"T_83b96_row5_col2\" class=\"data row5 col2\" >0.1197</td>\n",
       "      <td id=\"T_83b96_row5_col3\" class=\"data row5 col3\" >0.6600</td>\n",
       "      <td id=\"T_83b96_row5_col4\" class=\"data row5 col4\" >0.0123</td>\n",
       "      <td id=\"T_83b96_row5_col5\" class=\"data row5 col5\" >0.0104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_83b96_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_83b96_row6_col0\" class=\"data row6 col0\" >0.0896</td>\n",
       "      <td id=\"T_83b96_row6_col1\" class=\"data row6 col1\" >0.0140</td>\n",
       "      <td id=\"T_83b96_row6_col2\" class=\"data row6 col2\" >0.1185</td>\n",
       "      <td id=\"T_83b96_row6_col3\" class=\"data row6 col3\" >0.6725</td>\n",
       "      <td id=\"T_83b96_row6_col4\" class=\"data row6 col4\" >0.0122</td>\n",
       "      <td id=\"T_83b96_row6_col5\" class=\"data row6 col5\" >0.0102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_83b96_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_83b96_row7_col0\" class=\"data row7 col0\" >0.0864</td>\n",
       "      <td id=\"T_83b96_row7_col1\" class=\"data row7 col1\" >0.0124</td>\n",
       "      <td id=\"T_83b96_row7_col2\" class=\"data row7 col2\" >0.1113</td>\n",
       "      <td id=\"T_83b96_row7_col3\" class=\"data row7 col3\" >0.6799</td>\n",
       "      <td id=\"T_83b96_row7_col4\" class=\"data row7 col4\" >0.0114</td>\n",
       "      <td id=\"T_83b96_row7_col5\" class=\"data row7 col5\" >0.0098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_83b96_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_83b96_row8_col0\" class=\"data row8 col0\" >0.0912</td>\n",
       "      <td id=\"T_83b96_row8_col1\" class=\"data row8 col1\" >0.0148</td>\n",
       "      <td id=\"T_83b96_row8_col2\" class=\"data row8 col2\" >0.1218</td>\n",
       "      <td id=\"T_83b96_row8_col3\" class=\"data row8 col3\" >0.6591</td>\n",
       "      <td id=\"T_83b96_row8_col4\" class=\"data row8 col4\" >0.0125</td>\n",
       "      <td id=\"T_83b96_row8_col5\" class=\"data row8 col5\" >0.0104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_83b96_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_83b96_row9_col0\" class=\"data row9 col0\" >0.0930</td>\n",
       "      <td id=\"T_83b96_row9_col1\" class=\"data row9 col1\" >0.0148</td>\n",
       "      <td id=\"T_83b96_row9_col2\" class=\"data row9 col2\" >0.1217</td>\n",
       "      <td id=\"T_83b96_row9_col3\" class=\"data row9 col3\" >0.6447</td>\n",
       "      <td id=\"T_83b96_row9_col4\" class=\"data row9 col4\" >0.0125</td>\n",
       "      <td id=\"T_83b96_row9_col5\" class=\"data row9 col5\" >0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_83b96_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_83b96_row10_col0\" class=\"data row10 col0\" >0.0906</td>\n",
       "      <td id=\"T_83b96_row10_col1\" class=\"data row10 col1\" >0.0141</td>\n",
       "      <td id=\"T_83b96_row10_col2\" class=\"data row10 col2\" >0.1187</td>\n",
       "      <td id=\"T_83b96_row10_col3\" class=\"data row10 col3\" >0.6635</td>\n",
       "      <td id=\"T_83b96_row10_col4\" class=\"data row10 col4\" >0.0122</td>\n",
       "      <td id=\"T_83b96_row10_col5\" class=\"data row10 col5\" >0.0103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_83b96_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_83b96_row11_col0\" class=\"data row11 col0\" >0.0023</td>\n",
       "      <td id=\"T_83b96_row11_col1\" class=\"data row11 col1\" >0.0008</td>\n",
       "      <td id=\"T_83b96_row11_col2\" class=\"data row11 col2\" >0.0033</td>\n",
       "      <td id=\"T_83b96_row11_col3\" class=\"data row11 col3\" >0.0124</td>\n",
       "      <td id=\"T_83b96_row11_col4\" class=\"data row11 col4\" >0.0004</td>\n",
       "      <td id=\"T_83b96_row11_col5\" class=\"data row11 col5\" >0.0003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9f395d8f70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "catboost = create_model(\"catboost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:27:48.927222Z",
     "start_time": "2022-07-17T04:27:48.921245Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "NHL2zciizZsI",
    "outputId": "d606ad03-ecd5-487b-b205-bfc7a841f3a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x7f9f2cc71df0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T-dvDHxCzZsU"
   },
   "source": [
    "# 8.2 Light Gradient Boosting Machine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:27:49.657949Z",
     "start_time": "2022-07-17T04:27:48.930215Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "NC7OVDVrzZsX",
    "outputId": "a5abc702-d270-4134-892a-e9ecf82bdebb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_b5034_row10_col0, #T_b5034_row10_col1, #T_b5034_row10_col2, #T_b5034_row10_col3, #T_b5034_row10_col4, #T_b5034_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_b5034\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b5034_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_b5034_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_b5034_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_b5034_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_b5034_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_b5034_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b5034_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_b5034_row0_col0\" class=\"data row0 col0\" >0.0880</td>\n",
       "      <td id=\"T_b5034_row0_col1\" class=\"data row0 col1\" >0.0135</td>\n",
       "      <td id=\"T_b5034_row0_col2\" class=\"data row0 col2\" >0.1160</td>\n",
       "      <td id=\"T_b5034_row0_col3\" class=\"data row0 col3\" >0.6705</td>\n",
       "      <td id=\"T_b5034_row0_col4\" class=\"data row0 col4\" >0.0119</td>\n",
       "      <td id=\"T_b5034_row0_col5\" class=\"data row0 col5\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5034_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_b5034_row1_col0\" class=\"data row1 col0\" >0.0938</td>\n",
       "      <td id=\"T_b5034_row1_col1\" class=\"data row1 col1\" >0.0151</td>\n",
       "      <td id=\"T_b5034_row1_col2\" class=\"data row1 col2\" >0.1230</td>\n",
       "      <td id=\"T_b5034_row1_col3\" class=\"data row1 col3\" >0.6612</td>\n",
       "      <td id=\"T_b5034_row1_col4\" class=\"data row1 col4\" >0.0126</td>\n",
       "      <td id=\"T_b5034_row1_col5\" class=\"data row1 col5\" >0.0107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5034_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_b5034_row2_col0\" class=\"data row2 col0\" >0.0897</td>\n",
       "      <td id=\"T_b5034_row2_col1\" class=\"data row2 col1\" >0.0138</td>\n",
       "      <td id=\"T_b5034_row2_col2\" class=\"data row2 col2\" >0.1175</td>\n",
       "      <td id=\"T_b5034_row2_col3\" class=\"data row2 col3\" >0.6703</td>\n",
       "      <td id=\"T_b5034_row2_col4\" class=\"data row2 col4\" >0.0120</td>\n",
       "      <td id=\"T_b5034_row2_col5\" class=\"data row2 col5\" >0.0102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5034_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_b5034_row3_col0\" class=\"data row3 col0\" >0.0934</td>\n",
       "      <td id=\"T_b5034_row3_col1\" class=\"data row3 col1\" >0.0144</td>\n",
       "      <td id=\"T_b5034_row3_col2\" class=\"data row3 col2\" >0.1199</td>\n",
       "      <td id=\"T_b5034_row3_col3\" class=\"data row3 col3\" >0.6543</td>\n",
       "      <td id=\"T_b5034_row3_col4\" class=\"data row3 col4\" >0.0123</td>\n",
       "      <td id=\"T_b5034_row3_col5\" class=\"data row3 col5\" >0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5034_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_b5034_row4_col0\" class=\"data row4 col0\" >0.0924</td>\n",
       "      <td id=\"T_b5034_row4_col1\" class=\"data row4 col1\" >0.0148</td>\n",
       "      <td id=\"T_b5034_row4_col2\" class=\"data row4 col2\" >0.1218</td>\n",
       "      <td id=\"T_b5034_row4_col3\" class=\"data row4 col3\" >0.6378</td>\n",
       "      <td id=\"T_b5034_row4_col4\" class=\"data row4 col4\" >0.0125</td>\n",
       "      <td id=\"T_b5034_row4_col5\" class=\"data row4 col5\" >0.0105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5034_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_b5034_row5_col0\" class=\"data row5 col0\" >0.0927</td>\n",
       "      <td id=\"T_b5034_row5_col1\" class=\"data row5 col1\" >0.0146</td>\n",
       "      <td id=\"T_b5034_row5_col2\" class=\"data row5 col2\" >0.1207</td>\n",
       "      <td id=\"T_b5034_row5_col3\" class=\"data row5 col3\" >0.6546</td>\n",
       "      <td id=\"T_b5034_row5_col4\" class=\"data row5 col4\" >0.0124</td>\n",
       "      <td id=\"T_b5034_row5_col5\" class=\"data row5 col5\" >0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5034_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_b5034_row6_col0\" class=\"data row6 col0\" >0.0902</td>\n",
       "      <td id=\"T_b5034_row6_col1\" class=\"data row6 col1\" >0.0142</td>\n",
       "      <td id=\"T_b5034_row6_col2\" class=\"data row6 col2\" >0.1192</td>\n",
       "      <td id=\"T_b5034_row6_col3\" class=\"data row6 col3\" >0.6683</td>\n",
       "      <td id=\"T_b5034_row6_col4\" class=\"data row6 col4\" >0.0122</td>\n",
       "      <td id=\"T_b5034_row6_col5\" class=\"data row6 col5\" >0.0103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5034_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_b5034_row7_col0\" class=\"data row7 col0\" >0.0877</td>\n",
       "      <td id=\"T_b5034_row7_col1\" class=\"data row7 col1\" >0.0128</td>\n",
       "      <td id=\"T_b5034_row7_col2\" class=\"data row7 col2\" >0.1133</td>\n",
       "      <td id=\"T_b5034_row7_col3\" class=\"data row7 col3\" >0.6684</td>\n",
       "      <td id=\"T_b5034_row7_col4\" class=\"data row7 col4\" >0.0116</td>\n",
       "      <td id=\"T_b5034_row7_col5\" class=\"data row7 col5\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5034_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_b5034_row8_col0\" class=\"data row8 col0\" >0.0918</td>\n",
       "      <td id=\"T_b5034_row8_col1\" class=\"data row8 col1\" >0.0149</td>\n",
       "      <td id=\"T_b5034_row8_col2\" class=\"data row8 col2\" >0.1219</td>\n",
       "      <td id=\"T_b5034_row8_col3\" class=\"data row8 col3\" >0.6584</td>\n",
       "      <td id=\"T_b5034_row8_col4\" class=\"data row8 col4\" >0.0125</td>\n",
       "      <td id=\"T_b5034_row8_col5\" class=\"data row8 col5\" >0.0105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5034_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_b5034_row9_col0\" class=\"data row9 col0\" >0.0928</td>\n",
       "      <td id=\"T_b5034_row9_col1\" class=\"data row9 col1\" >0.0148</td>\n",
       "      <td id=\"T_b5034_row9_col2\" class=\"data row9 col2\" >0.1218</td>\n",
       "      <td id=\"T_b5034_row9_col3\" class=\"data row9 col3\" >0.6443</td>\n",
       "      <td id=\"T_b5034_row9_col4\" class=\"data row9 col4\" >0.0125</td>\n",
       "      <td id=\"T_b5034_row9_col5\" class=\"data row9 col5\" >0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5034_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_b5034_row10_col0\" class=\"data row10 col0\" >0.0912</td>\n",
       "      <td id=\"T_b5034_row10_col1\" class=\"data row10 col1\" >0.0143</td>\n",
       "      <td id=\"T_b5034_row10_col2\" class=\"data row10 col2\" >0.1195</td>\n",
       "      <td id=\"T_b5034_row10_col3\" class=\"data row10 col3\" >0.6588</td>\n",
       "      <td id=\"T_b5034_row10_col4\" class=\"data row10 col4\" >0.0123</td>\n",
       "      <td id=\"T_b5034_row10_col5\" class=\"data row10 col5\" >0.0104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5034_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_b5034_row11_col0\" class=\"data row11 col0\" >0.0021</td>\n",
       "      <td id=\"T_b5034_row11_col1\" class=\"data row11 col1\" >0.0007</td>\n",
       "      <td id=\"T_b5034_row11_col2\" class=\"data row11 col2\" >0.0029</td>\n",
       "      <td id=\"T_b5034_row11_col3\" class=\"data row11 col3\" >0.0107</td>\n",
       "      <td id=\"T_b5034_row11_col4\" class=\"data row11 col4\" >0.0003</td>\n",
       "      <td id=\"T_b5034_row11_col5\" class=\"data row11 col5\" >0.0002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9f2cc574f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lightgbm = create_model(\"lightgbm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NsOBIl8szZs1"
   },
   "source": [
    "Notice that the Mean score of all models matches with the score printed in `compare_models()`. This is because the metrics printed in the `compare_models()` score grid are the average scores across all CV folds. Similar to `compare_models()`, if you want to change the fold parameter from the default value of 10 to a different value then you can use the `fold` parameter. For Example: `create_model('lightgbm', fold = 5)` to create Decision Tree using 5 fold cross validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8RZB8YllzZs7"
   },
   "source": [
    "# 9.0 Tune a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AYYWC1X5zZs-"
   },
   "source": [
    "When a model is created using the `create_model` function it uses the default hyperparameters to train the model. In order to tune hyperparameters, the `tune_model` function is used. This function automatically tunes the hyperparameters of a model using `Random Grid Search` on a pre-defined search space. The output prints a score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold. To use the custom search grid, you can pass `custom_grid` parameter in the `tune_model` function (see 9.2 LightGBM tuning below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5uUSmZLGzZtB"
   },
   "source": [
    "# 9.1 CatBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:27:58.019080Z",
     "start_time": "2022-07-17T04:27:50.012518Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "XM7qgcGIzZtE",
    "outputId": "e87c7fac-4dfe-4733-ddc5-d44359b0ea0d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_ce159_row10_col0, #T_ce159_row10_col1, #T_ce159_row10_col2, #T_ce159_row10_col3, #T_ce159_row10_col4, #T_ce159_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_ce159\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ce159_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_ce159_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_ce159_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_ce159_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_ce159_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_ce159_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ce159_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_ce159_row0_col0\" class=\"data row0 col0\" >0.0882</td>\n",
       "      <td id=\"T_ce159_row0_col1\" class=\"data row0 col1\" >0.0135</td>\n",
       "      <td id=\"T_ce159_row0_col2\" class=\"data row0 col2\" >0.1162</td>\n",
       "      <td id=\"T_ce159_row0_col3\" class=\"data row0 col3\" >0.6693</td>\n",
       "      <td id=\"T_ce159_row0_col4\" class=\"data row0 col4\" >0.0119</td>\n",
       "      <td id=\"T_ce159_row0_col5\" class=\"data row0 col5\" >0.0101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce159_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_ce159_row1_col0\" class=\"data row1 col0\" >0.0943</td>\n",
       "      <td id=\"T_ce159_row1_col1\" class=\"data row1 col1\" >0.0151</td>\n",
       "      <td id=\"T_ce159_row1_col2\" class=\"data row1 col2\" >0.1231</td>\n",
       "      <td id=\"T_ce159_row1_col3\" class=\"data row1 col3\" >0.6608</td>\n",
       "      <td id=\"T_ce159_row1_col4\" class=\"data row1 col4\" >0.0126</td>\n",
       "      <td id=\"T_ce159_row1_col5\" class=\"data row1 col5\" >0.0108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce159_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_ce159_row2_col0\" class=\"data row2 col0\" >0.0882</td>\n",
       "      <td id=\"T_ce159_row2_col1\" class=\"data row2 col1\" >0.0132</td>\n",
       "      <td id=\"T_ce159_row2_col2\" class=\"data row2 col2\" >0.1151</td>\n",
       "      <td id=\"T_ce159_row2_col3\" class=\"data row2 col3\" >0.6841</td>\n",
       "      <td id=\"T_ce159_row2_col4\" class=\"data row2 col4\" >0.0118</td>\n",
       "      <td id=\"T_ce159_row2_col5\" class=\"data row2 col5\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce159_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_ce159_row3_col0\" class=\"data row3 col0\" >0.0927</td>\n",
       "      <td id=\"T_ce159_row3_col1\" class=\"data row3 col1\" >0.0141</td>\n",
       "      <td id=\"T_ce159_row3_col2\" class=\"data row3 col2\" >0.1189</td>\n",
       "      <td id=\"T_ce159_row3_col3\" class=\"data row3 col3\" >0.6602</td>\n",
       "      <td id=\"T_ce159_row3_col4\" class=\"data row3 col4\" >0.0122</td>\n",
       "      <td id=\"T_ce159_row3_col5\" class=\"data row3 col5\" >0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce159_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_ce159_row4_col0\" class=\"data row4 col0\" >0.0925</td>\n",
       "      <td id=\"T_ce159_row4_col1\" class=\"data row4 col1\" >0.0148</td>\n",
       "      <td id=\"T_ce159_row4_col2\" class=\"data row4 col2\" >0.1215</td>\n",
       "      <td id=\"T_ce159_row4_col3\" class=\"data row4 col3\" >0.6395</td>\n",
       "      <td id=\"T_ce159_row4_col4\" class=\"data row4 col4\" >0.0124</td>\n",
       "      <td id=\"T_ce159_row4_col5\" class=\"data row4 col5\" >0.0105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce159_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_ce159_row5_col0\" class=\"data row5 col0\" >0.0927</td>\n",
       "      <td id=\"T_ce159_row5_col1\" class=\"data row5 col1\" >0.0145</td>\n",
       "      <td id=\"T_ce159_row5_col2\" class=\"data row5 col2\" >0.1205</td>\n",
       "      <td id=\"T_ce159_row5_col3\" class=\"data row5 col3\" >0.6560</td>\n",
       "      <td id=\"T_ce159_row5_col4\" class=\"data row5 col4\" >0.0124</td>\n",
       "      <td id=\"T_ce159_row5_col5\" class=\"data row5 col5\" >0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce159_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_ce159_row6_col0\" class=\"data row6 col0\" >0.0899</td>\n",
       "      <td id=\"T_ce159_row6_col1\" class=\"data row6 col1\" >0.0141</td>\n",
       "      <td id=\"T_ce159_row6_col2\" class=\"data row6 col2\" >0.1189</td>\n",
       "      <td id=\"T_ce159_row6_col3\" class=\"data row6 col3\" >0.6698</td>\n",
       "      <td id=\"T_ce159_row6_col4\" class=\"data row6 col4\" >0.0122</td>\n",
       "      <td id=\"T_ce159_row6_col5\" class=\"data row6 col5\" >0.0103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce159_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_ce159_row7_col0\" class=\"data row7 col0\" >0.0877</td>\n",
       "      <td id=\"T_ce159_row7_col1\" class=\"data row7 col1\" >0.0126</td>\n",
       "      <td id=\"T_ce159_row7_col2\" class=\"data row7 col2\" >0.1122</td>\n",
       "      <td id=\"T_ce159_row7_col3\" class=\"data row7 col3\" >0.6746</td>\n",
       "      <td id=\"T_ce159_row7_col4\" class=\"data row7 col4\" >0.0115</td>\n",
       "      <td id=\"T_ce159_row7_col5\" class=\"data row7 col5\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce159_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_ce159_row8_col0\" class=\"data row8 col0\" >0.0919</td>\n",
       "      <td id=\"T_ce159_row8_col1\" class=\"data row8 col1\" >0.0149</td>\n",
       "      <td id=\"T_ce159_row8_col2\" class=\"data row8 col2\" >0.1222</td>\n",
       "      <td id=\"T_ce159_row8_col3\" class=\"data row8 col3\" >0.6568</td>\n",
       "      <td id=\"T_ce159_row8_col4\" class=\"data row8 col4\" >0.0126</td>\n",
       "      <td id=\"T_ce159_row8_col5\" class=\"data row8 col5\" >0.0105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce159_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_ce159_row9_col0\" class=\"data row9 col0\" >0.0930</td>\n",
       "      <td id=\"T_ce159_row9_col1\" class=\"data row9 col1\" >0.0145</td>\n",
       "      <td id=\"T_ce159_row9_col2\" class=\"data row9 col2\" >0.1204</td>\n",
       "      <td id=\"T_ce159_row9_col3\" class=\"data row9 col3\" >0.6523</td>\n",
       "      <td id=\"T_ce159_row9_col4\" class=\"data row9 col4\" >0.0124</td>\n",
       "      <td id=\"T_ce159_row9_col5\" class=\"data row9 col5\" >0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce159_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_ce159_row10_col0\" class=\"data row10 col0\" >0.0911</td>\n",
       "      <td id=\"T_ce159_row10_col1\" class=\"data row10 col1\" >0.0141</td>\n",
       "      <td id=\"T_ce159_row10_col2\" class=\"data row10 col2\" >0.1189</td>\n",
       "      <td id=\"T_ce159_row10_col3\" class=\"data row10 col3\" >0.6623</td>\n",
       "      <td id=\"T_ce159_row10_col4\" class=\"data row10 col4\" >0.0122</td>\n",
       "      <td id=\"T_ce159_row10_col5\" class=\"data row10 col5\" >0.0104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce159_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_ce159_row11_col0\" class=\"data row11 col0\" >0.0023</td>\n",
       "      <td id=\"T_ce159_row11_col1\" class=\"data row11 col1\" >0.0008</td>\n",
       "      <td id=\"T_ce159_row11_col2\" class=\"data row11 col2\" >0.0033</td>\n",
       "      <td id=\"T_ce159_row11_col3\" class=\"data row11 col3\" >0.0119</td>\n",
       "      <td id=\"T_ce159_row11_col4\" class=\"data row11 col4\" >0.0003</td>\n",
       "      <td id=\"T_ce159_row11_col5\" class=\"data row11 col5\" >0.0003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9f3f950460>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n",
      "Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n"
     ]
    }
   ],
   "source": [
    "tuned_catboost = tune_model(catboost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:27:58.029072Z",
     "start_time": "2022-07-17T04:27:58.022077Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "Ul0HJFoRzZtU",
    "outputId": "5ac4ab0b-c746-4a9f-a286-3e5cd2bbe536"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x7f9f395d58e0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_catboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3kvdvfdUzZtj"
   },
   "source": [
    "# 9.2 Light Gradient Boosting Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:27:58.052049Z",
     "start_time": "2022-07-17T04:27:58.031071Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# grid for HPO\n",
    "lgbm_params = {\n",
    "    \"num_leaves\": np.arange(10, 200, 10),\n",
    "    \"max_depth\": [int(x) for x in np.linspace(10, 110, num=11)],\n",
    "    \"learning_rate\": np.arange(0.1, 1, 0.1),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:01.009800Z",
     "start_time": "2022-07-17T04:27:58.055046Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "s1agvmDFzZtm",
    "outputId": "7cfab1a7-e7c2-40df-ad1e-a40067bcc4e0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4659d_row10_col0, #T_4659d_row10_col1, #T_4659d_row10_col2, #T_4659d_row10_col3, #T_4659d_row10_col4, #T_4659d_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4659d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4659d_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_4659d_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_4659d_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_4659d_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_4659d_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_4659d_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4659d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_4659d_row0_col0\" class=\"data row0 col0\" >0.0887</td>\n",
       "      <td id=\"T_4659d_row0_col1\" class=\"data row0 col1\" >0.0138</td>\n",
       "      <td id=\"T_4659d_row0_col2\" class=\"data row0 col2\" >0.1173</td>\n",
       "      <td id=\"T_4659d_row0_col3\" class=\"data row0 col3\" >0.6632</td>\n",
       "      <td id=\"T_4659d_row0_col4\" class=\"data row0 col4\" >0.0121</td>\n",
       "      <td id=\"T_4659d_row0_col5\" class=\"data row0 col5\" >0.0101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4659d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_4659d_row1_col0\" class=\"data row1 col0\" >0.0946</td>\n",
       "      <td id=\"T_4659d_row1_col1\" class=\"data row1 col1\" >0.0152</td>\n",
       "      <td id=\"T_4659d_row1_col2\" class=\"data row1 col2\" >0.1232</td>\n",
       "      <td id=\"T_4659d_row1_col3\" class=\"data row1 col3\" >0.6598</td>\n",
       "      <td id=\"T_4659d_row1_col4\" class=\"data row1 col4\" >0.0127</td>\n",
       "      <td id=\"T_4659d_row1_col5\" class=\"data row1 col5\" >0.0108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4659d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_4659d_row2_col0\" class=\"data row2 col0\" >0.0895</td>\n",
       "      <td id=\"T_4659d_row2_col1\" class=\"data row2 col1\" >0.0137</td>\n",
       "      <td id=\"T_4659d_row2_col2\" class=\"data row2 col2\" >0.1169</td>\n",
       "      <td id=\"T_4659d_row2_col3\" class=\"data row2 col3\" >0.6741</td>\n",
       "      <td id=\"T_4659d_row2_col4\" class=\"data row2 col4\" >0.0120</td>\n",
       "      <td id=\"T_4659d_row2_col5\" class=\"data row2 col5\" >0.0102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4659d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_4659d_row3_col0\" class=\"data row3 col0\" >0.0929</td>\n",
       "      <td id=\"T_4659d_row3_col1\" class=\"data row3 col1\" >0.0143</td>\n",
       "      <td id=\"T_4659d_row3_col2\" class=\"data row3 col2\" >0.1195</td>\n",
       "      <td id=\"T_4659d_row3_col3\" class=\"data row3 col3\" >0.6568</td>\n",
       "      <td id=\"T_4659d_row3_col4\" class=\"data row3 col4\" >0.0123</td>\n",
       "      <td id=\"T_4659d_row3_col5\" class=\"data row3 col5\" >0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4659d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_4659d_row4_col0\" class=\"data row4 col0\" >0.0936</td>\n",
       "      <td id=\"T_4659d_row4_col1\" class=\"data row4 col1\" >0.0151</td>\n",
       "      <td id=\"T_4659d_row4_col2\" class=\"data row4 col2\" >0.1227</td>\n",
       "      <td id=\"T_4659d_row4_col3\" class=\"data row4 col3\" >0.6322</td>\n",
       "      <td id=\"T_4659d_row4_col4\" class=\"data row4 col4\" >0.0126</td>\n",
       "      <td id=\"T_4659d_row4_col5\" class=\"data row4 col5\" >0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4659d_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_4659d_row5_col0\" class=\"data row5 col0\" >0.0920</td>\n",
       "      <td id=\"T_4659d_row5_col1\" class=\"data row5 col1\" >0.0144</td>\n",
       "      <td id=\"T_4659d_row5_col2\" class=\"data row5 col2\" >0.1200</td>\n",
       "      <td id=\"T_4659d_row5_col3\" class=\"data row5 col3\" >0.6588</td>\n",
       "      <td id=\"T_4659d_row5_col4\" class=\"data row5 col4\" >0.0123</td>\n",
       "      <td id=\"T_4659d_row5_col5\" class=\"data row5 col5\" >0.0105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4659d_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_4659d_row6_col0\" class=\"data row6 col0\" >0.0887</td>\n",
       "      <td id=\"T_4659d_row6_col1\" class=\"data row6 col1\" >0.0139</td>\n",
       "      <td id=\"T_4659d_row6_col2\" class=\"data row6 col2\" >0.1179</td>\n",
       "      <td id=\"T_4659d_row6_col3\" class=\"data row6 col3\" >0.6757</td>\n",
       "      <td id=\"T_4659d_row6_col4\" class=\"data row6 col4\" >0.0121</td>\n",
       "      <td id=\"T_4659d_row6_col5\" class=\"data row6 col5\" >0.0101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4659d_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_4659d_row7_col0\" class=\"data row7 col0\" >0.0879</td>\n",
       "      <td id=\"T_4659d_row7_col1\" class=\"data row7 col1\" >0.0128</td>\n",
       "      <td id=\"T_4659d_row7_col2\" class=\"data row7 col2\" >0.1131</td>\n",
       "      <td id=\"T_4659d_row7_col3\" class=\"data row7 col3\" >0.6695</td>\n",
       "      <td id=\"T_4659d_row7_col4\" class=\"data row7 col4\" >0.0116</td>\n",
       "      <td id=\"T_4659d_row7_col5\" class=\"data row7 col5\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4659d_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_4659d_row8_col0\" class=\"data row8 col0\" >0.0912</td>\n",
       "      <td id=\"T_4659d_row8_col1\" class=\"data row8 col1\" >0.0147</td>\n",
       "      <td id=\"T_4659d_row8_col2\" class=\"data row8 col2\" >0.1211</td>\n",
       "      <td id=\"T_4659d_row8_col3\" class=\"data row8 col3\" >0.6628</td>\n",
       "      <td id=\"T_4659d_row8_col4\" class=\"data row8 col4\" >0.0124</td>\n",
       "      <td id=\"T_4659d_row8_col5\" class=\"data row8 col5\" >0.0104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4659d_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_4659d_row9_col0\" class=\"data row9 col0\" >0.0932</td>\n",
       "      <td id=\"T_4659d_row9_col1\" class=\"data row9 col1\" >0.0148</td>\n",
       "      <td id=\"T_4659d_row9_col2\" class=\"data row9 col2\" >0.1216</td>\n",
       "      <td id=\"T_4659d_row9_col3\" class=\"data row9 col3\" >0.6451</td>\n",
       "      <td id=\"T_4659d_row9_col4\" class=\"data row9 col4\" >0.0125</td>\n",
       "      <td id=\"T_4659d_row9_col5\" class=\"data row9 col5\" >0.0106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4659d_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_4659d_row10_col0\" class=\"data row10 col0\" >0.0912</td>\n",
       "      <td id=\"T_4659d_row10_col1\" class=\"data row10 col1\" >0.0142</td>\n",
       "      <td id=\"T_4659d_row10_col2\" class=\"data row10 col2\" >0.1193</td>\n",
       "      <td id=\"T_4659d_row10_col3\" class=\"data row10 col3\" >0.6598</td>\n",
       "      <td id=\"T_4659d_row10_col4\" class=\"data row10 col4\" >0.0122</td>\n",
       "      <td id=\"T_4659d_row10_col5\" class=\"data row10 col5\" >0.0104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4659d_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_4659d_row11_col0\" class=\"data row11 col0\" >0.0022</td>\n",
       "      <td id=\"T_4659d_row11_col1\" class=\"data row11 col1\" >0.0007</td>\n",
       "      <td id=\"T_4659d_row11_col2\" class=\"data row11 col2\" >0.0029</td>\n",
       "      <td id=\"T_4659d_row11_col3\" class=\"data row11 col3\" >0.0125</td>\n",
       "      <td id=\"T_4659d_row11_col4\" class=\"data row11 col4\" >0.0003</td>\n",
       "      <td id=\"T_4659d_row11_col5\" class=\"data row11 col5\" >0.0003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9f39688c70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
     ]
    }
   ],
   "source": [
    "tuned_lightgbm = tune_model(lightgbm, custom_grid=lgbm_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:01.025788Z",
     "start_time": "2022-07-17T04:28:01.013797Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBMRegressor(max_depth=60, num_leaves=20, random_state=3434)\n"
     ]
    }
   ],
   "source": [
    "print(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TcSjA7SUzZt-"
   },
   "source": [
    "By default, `tune_model` optimizes `R2` but this can be changed using optimize parameter. For example: tune_model(rf, optimize = 'MAE') will search for the hyperparameters of a Random Forrest Regressor that results in the lowest `MAE` instead of highest `R2`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HR-mHgtCzZuE"
   },
   "source": [
    "# 10.0 Plot a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N6i7Ggg_zZuH"
   },
   "source": [
    "Before model finalization, the `plot_model()` function can be used to analyze the performance across different aspects such as Residuals Plot, Prediction Error, Feature Importance etc. This function takes a trained model object and returns a plot based on the test / hold-out set. \n",
    "\n",
    "There are over 10 plots available, please see the `plot_model()` docstring for the list of available plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HJCYRQj9zZuU"
   },
   "source": [
    "# 10.1 Residual Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:04.716751Z",
     "start_time": "2022-07-17T04:28:03.123867Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "ml-qe8dTzZuX",
    "outputId": "4c69b0b8-8e82-4003-f0e2-158b19a79f34"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_catboost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rM9dWgfVzZuh"
   },
   "source": [
    "# 10.2 Prediction Error Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:05.282825Z",
     "start_time": "2022-07-17T04:28:04.719745Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "GPwWRYehzZuk",
    "outputId": "e3dfe255-fa08-42f7-e556-74c7909f6e6e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_catboost, plot=\"error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dWu_EtTGzZuu"
   },
   "source": [
    "# 10.3 Feature Importance Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:05.730926Z",
     "start_time": "2022-07-17T04:28:05.286828Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "7Yh852PPzZux",
    "outputId": "38295169-5000-4a07-e71d-76de9877ab42"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_catboost, plot=\"feature\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qI_tk-8RzZu8"
   },
   "source": [
    "*Another* way to analyze the performance of models is to use the `evaluate_model()` function which displays a user interface for all of the available plots for a given model. It internally uses the `plot_model()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:05.968668Z",
     "start_time": "2022-07-17T04:28:05.733913Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 398,
     "referenced_widgets": [
      "2f905c9057e849cfb68d8b4a73a9ae2c",
      "4705ae97c5c341db8a7efcc7e4334c79",
      "202f5f0dfbea4f6cb6a75e4aebd370c7",
      "4c0b5f15356140e9bd6ed3a2e0850baa",
      "63e7b6e04e3d43118698abaca0973960",
      "b7731479f5f141289a4939cb73adfb28",
      "5eb7949ac4a140118e9ee6be49921284",
      "19ea86ac89a349e281011fbd327c29c0",
      "c9bbc67e75d1477e8cd7a64502b98704",
      "5334e09f4bed4a0ba45635300e76027e",
      "14244d0fca4f40a8b3552675869748a3",
      "baf068abf86f44a9a9502f79dfe17eb5",
      "3495c4e24f1d4b22af1117ea2403a4fa",
      "83a10da0205742d9a383671a0be02606",
      "4ae774bce02d4c1ab1351cd75b25cf17",
      "ee5416de1bb1462e87b2fa6f9065d646",
      "36a9c708b65e498586a07d526e6a5d06",
      "aeb87dce945f4d588807575e9f6e64c3"
     ]
    },
    "colab_type": "code",
    "id": "J4ryBACHzZu_",
    "outputId": "246ab553-8043-4cbe-fe99-7ea00bb13aed"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a8bffb51b464ccaa39a566a0a638d3d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(tuned_catboost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CxKARgKAzZvJ"
   },
   "source": [
    "# 11.0 Predict on Test / Hold-out Sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r8k_rmplzZvL"
   },
   "source": [
    "Before finalizing the model, it is advisable to perform one final check by predicting the test/hold-out set and reviewing the evaluation metrics. If you look at the information grid in Section 6 above, you will see that 30% of the data has been separated out as a test/hold-out sample. All of the evaluation metrics we have seen above are cross-validated results based on training set (70%) only. Now, using our final trained model stored in the `tuned_catboost` variable we will predict the hold-out sample and evaluate the metrics to see if they are materially different than the CV results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:06.159664Z",
     "start_time": "2022-07-17T04:28:05.972669Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "ozTyeSjCzZvY",
    "outputId": "1cdf25f4-0988-40fa-c8e9-5695fec11f05"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_31639\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_31639_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_31639_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_31639_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_31639_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_31639_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_31639_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_31639_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_31639_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_31639_row0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "      <td id=\"T_31639_row0_col1\" class=\"data row0 col1\" >0.0907</td>\n",
       "      <td id=\"T_31639_row0_col2\" class=\"data row0 col2\" >0.0141</td>\n",
       "      <td id=\"T_31639_row0_col3\" class=\"data row0 col3\" >0.1189</td>\n",
       "      <td id=\"T_31639_row0_col4\" class=\"data row0 col4\" >0.6559</td>\n",
       "      <td id=\"T_31639_row0_col5\" class=\"data row0 col5\" >0.0122</td>\n",
       "      <td id=\"T_31639_row0_col6\" class=\"data row0 col6\" >0.0103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9f2c818fa0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_model(tuned_catboost);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J0PmhEQFzZvr"
   },
   "source": [
    "# 12.0 Finalize Model for Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Rtaj0uWgzZvx"
   },
   "source": [
    "Model finalization is the last step in the experiment. A normal machine learning workflow in PyCaret starts with `setup()`, followed by comparing all models using `compare_models()` and shortlisting a few candidate models (based on the metric of interest) to perform several modeling techniques such as hyperparameter tuning, ensembling, stacking etc. This workflow will eventually lead you to the best model for use in making predictions on new and unseen data. The `finalize_model()` function fits the model onto the complete dataset including the test/hold-out sample (30% in this case). The purpose of this function is to train the model on the complete dataset before it is deployed in production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:07.280941Z",
     "start_time": "2022-07-17T04:28:06.162661Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "UPk310pezZv0"
   },
   "outputs": [],
   "source": [
    "final_catboost = finalize_model(tuned_catboost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:07.292920Z",
     "start_time": "2022-07-17T04:28:07.284942Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "IGFUGDAPzZwF",
    "outputId": "ddaa33c4-f728-444a-fc33-47da5c37304a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(memory=Memory(location=/tmp/joblib),\n",
       "         steps=[(&#x27;iterative_imputer&#x27;,\n",
       "                 TransformerWrapper(transformer=IterativeImputer(cat_estimator=LGBMClassifier(random_state=3434),\n",
       "                                                                 cat_estimator_prepare_for_categoricals_type=(LGBMClassifier(random_state=3434),\n",
       "                                                                                                              {}),\n",
       "                                                                 categorical_indices=[],\n",
       "                                                                 max_iter=5,\n",
       "                                                                 num_estimator=LGBMRegressor(random_state=3434),\n",
       "                                                                 num_estimator_prepare_for_categoricals_type=(LGBMRegressor(random_state=3434),\n",
       "                                                                                                              {}),\n",
       "                                                                 random_state=3434,\n",
       "                                                                 skip_complete=True))),\n",
       "                (&#x27;actual_estimator&#x27;,\n",
       "                 &lt;catboost.core.CatBoostRegressor object at 0x7f9f2cd8a8b0&gt;)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(memory=Memory(location=/tmp/joblib),\n",
       "         steps=[(&#x27;iterative_imputer&#x27;,\n",
       "                 TransformerWrapper(transformer=IterativeImputer(cat_estimator=LGBMClassifier(random_state=3434),\n",
       "                                                                 cat_estimator_prepare_for_categoricals_type=(LGBMClassifier(random_state=3434),\n",
       "                                                                                                              {}),\n",
       "                                                                 categorical_indices=[],\n",
       "                                                                 max_iter=5,\n",
       "                                                                 num_estimator=LGBMRegressor(random_state=3434),\n",
       "                                                                 num_estimator_prepare_for_categoricals_type=(LGBMRegressor(random_state=3434),\n",
       "                                                                                                              {}),\n",
       "                                                                 random_state=3434,\n",
       "                                                                 skip_complete=True))),\n",
       "                (&#x27;actual_estimator&#x27;,\n",
       "                 &lt;catboost.core.CatBoostRegressor object at 0x7f9f2cd8a8b0&gt;)])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">iterative_imputer: TransformerWrapper</label><div class=\"sk-toggleable__content\"><pre>TransformerWrapper(transformer=IterativeImputer(cat_estimator=LGBMClassifier(random_state=3434),\n",
       "                                                cat_estimator_prepare_for_categoricals_type=(LGBMClassifier(random_state=3434),\n",
       "                                                                                             {}),\n",
       "                                                categorical_indices=[],\n",
       "                                                max_iter=5,\n",
       "                                                num_estimator=LGBMRegressor(random_state=3434),\n",
       "                                                num_estimator_prepare_for_categoricals_type=(LGBMRegressor(random_state=3434),\n",
       "                                                                                             {}),\n",
       "                                                random_state=3434,\n",
       "                                                skip_complete=True))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: IterativeImputer</label><div class=\"sk-toggleable__content\"><pre>IterativeImputer(cat_estimator=LGBMClassifier(random_state=3434),\n",
       "                 cat_estimator_prepare_for_categoricals_type=(LGBMClassifier(random_state=3434),\n",
       "                                                              {}),\n",
       "                 categorical_indices=[], max_iter=5,\n",
       "                 num_estimator=LGBMRegressor(random_state=3434),\n",
       "                 num_estimator_prepare_for_categoricals_type=(LGBMRegressor(random_state=3434),\n",
       "                                                              {}),\n",
       "                 random_state=3434, skip_complete=True)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat_estimator: LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(random_state=3434)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(random_state=3434)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num_estimator: LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(random_state=3434)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(random_state=3434)</pre></div></div></div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostRegressor object at 0x7f9f2cd8a8b0&gt;</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(memory=Memory(location=/tmp/joblib),\n",
       "         steps=[('iterative_imputer',\n",
       "                 TransformerWrapper(transformer=IterativeImputer(cat_estimator=LGBMClassifier(random_state=3434),\n",
       "                                                                 cat_estimator_prepare_for_categoricals_type=(LGBMClassifier(random_state=3434),\n",
       "                                                                                                              {}),\n",
       "                                                                 categorical_indices=[],\n",
       "                                                                 max_iter=5,\n",
       "                                                                 num_estimator=LGBMRegressor(random_state=3434),\n",
       "                                                                 num_estimator_prepare_for_categoricals_type=(LGBMRegressor(random_state=3434),\n",
       "                                                                                                              {}),\n",
       "                                                                 random_state=3434,\n",
       "                                                                 skip_complete=True))),\n",
       "                ('actual_estimator',\n",
       "                 <catboost.core.CatBoostRegressor object at 0x7f9f2cd8a8b0>)])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_catboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QmJjaIkrzZwQ"
   },
   "source": [
    "**Caution:** One final word of caution. Once the model is finalized using `finalize_model()`, the entire dataset including the test/hold-out set is used for training. As such, if the model is used for predictions on the hold-out set after `finalize_model()` is used, the information grid printed will be misleading as you are trying to predict on the same data that was used for modeling. In order to demonstrate this point only, we will use `final_catboost` under `predict_model()` to compare the information grid with the one above in section 11. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:07.490232Z",
     "start_time": "2022-07-17T04:28:07.296925Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "bmYJRTAyzZwU",
    "outputId": "58e2f57b-abda-4166-c82b-af52ab6f18b8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_d3643\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d3643_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_d3643_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_d3643_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_d3643_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_d3643_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_d3643_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_d3643_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d3643_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_d3643_row0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "      <td id=\"T_d3643_row0_col1\" class=\"data row0 col1\" >0.0739</td>\n",
       "      <td id=\"T_d3643_row0_col2\" class=\"data row0 col2\" >0.0092</td>\n",
       "      <td id=\"T_d3643_row0_col3\" class=\"data row0 col3\" >0.0960</td>\n",
       "      <td id=\"T_d3643_row0_col4\" class=\"data row0 col4\" >0.7760</td>\n",
       "      <td id=\"T_d3643_row0_col5\" class=\"data row0 col5\" >0.0098</td>\n",
       "      <td id=\"T_d3643_row0_col6\" class=\"data row0 col6\" >0.0084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9f2cc13190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_model(final_catboost);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CgKSkSsZzZwv"
   },
   "source": [
    "# 13.0 Predict on Unseen Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6n7QFM94zZwy"
   },
   "source": [
    "The `predict_model()` function is also used to predict on the unseen dataset. The only difference from section 11 above is that this time we will pass the `data_unseen` parameter. `data_unseen` is the variable created at the beginning of the tutorial and contains 10% (600 samples) of the original dataset which was never exposed to PyCaret. (see section 5 for explanation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:07.737013Z",
     "start_time": "2022-07-17T04:28:07.494227Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "YdlpJUx0zZw4",
    "outputId": "5b45a2b5-c9a1-4d20-80f7-28211f07d586"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "      <th>petroMag_u</th>\n",
       "      <th>petroMag_g</th>\n",
       "      <th>petroMag_r</th>\n",
       "      <th>petroMag_i</th>\n",
       "      <th>petroMag_z</th>\n",
       "      <th>logMstar_S16</th>\n",
       "      <th>logSFR_SED_S16</th>\n",
       "      <th>prediction_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.026444</td>\n",
       "      <td>17.287437</td>\n",
       "      <td>15.680633</td>\n",
       "      <td>14.896384</td>\n",
       "      <td>14.504523</td>\n",
       "      <td>14.259768</td>\n",
       "      <td>10.440</td>\n",
       "      <td>-2.474</td>\n",
       "      <td>9.007159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.026444</td>\n",
       "      <td>17.639717</td>\n",
       "      <td>15.913450</td>\n",
       "      <td>15.068103</td>\n",
       "      <td>14.646886</td>\n",
       "      <td>14.366766</td>\n",
       "      <td>10.480</td>\n",
       "      <td>-0.537</td>\n",
       "      <td>9.078650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.026442</td>\n",
       "      <td>17.498098</td>\n",
       "      <td>15.600836</td>\n",
       "      <td>14.838774</td>\n",
       "      <td>14.425499</td>\n",
       "      <td>14.211129</td>\n",
       "      <td>10.457</td>\n",
       "      <td>-0.812</td>\n",
       "      <td>8.984698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.026439</td>\n",
       "      <td>17.077347</td>\n",
       "      <td>15.375058</td>\n",
       "      <td>14.623333</td>\n",
       "      <td>14.252543</td>\n",
       "      <td>13.963070</td>\n",
       "      <td>10.546</td>\n",
       "      <td>-2.747</td>\n",
       "      <td>9.041953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.026436</td>\n",
       "      <td>17.146229</td>\n",
       "      <td>15.888835</td>\n",
       "      <td>15.313497</td>\n",
       "      <td>14.995555</td>\n",
       "      <td>14.815013</td>\n",
       "      <td>10.183</td>\n",
       "      <td>-0.171</td>\n",
       "      <td>9.015403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Z  petroMag_u  petroMag_g  petroMag_r  petroMag_i  petroMag_z  \\\n",
       "0  0.026444   17.287437   15.680633   14.896384   14.504523   14.259768   \n",
       "1  0.026444   17.639717   15.913450   15.068103   14.646886   14.366766   \n",
       "2  0.026442   17.498098   15.600836   14.838774   14.425499   14.211129   \n",
       "3  0.026439   17.077347   15.375058   14.623333   14.252543   13.963070   \n",
       "4  0.026436   17.146229   15.888835   15.313497   14.995555   14.815013   \n",
       "\n",
       "   logMstar_S16  logSFR_SED_S16  prediction_label  \n",
       "0        10.440          -2.474          9.007159  \n",
       "1        10.480          -0.537          9.078650  \n",
       "2        10.457          -0.812          8.984698  \n",
       "3        10.546          -2.747          9.041953  \n",
       "4        10.183          -0.171          9.015403  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_predictions = predict_model(final_catboost, data=data_unseen)\n",
    "unseen_predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wZnpuHoDzZxG"
   },
   "source": [
    "The `Label` column is added onto the `data_unseen` set. Label is the predicted value using the `final_catboost` model. If you want predictions to be rounded, you can use `round` parameter inside `predict_model()`. You can also check the metrics on this since you have actual target column `Price` available. To do that we will use pycaret.utils module. See example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:07.749001Z",
     "start_time": "2022-07-17T04:28:07.739977Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f, a = plt.subplots()\n",
    "data.plot.scatter(x=\"logMstar_S16\", y=\"lgOH_MEDIAN_T04\", c=\"DarkBlue\", s=0.1, ax=a)\n",
    "unseen_predictions.plot.scatter(x=\"logMstar_S16\", y=\"prediction_label\", c=\"DarkRed\", s=0.1, ax=a)\n",
    "_ = a.set_ylabel(\"predicted log (O/H) + 12\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "os2dbiIrzZxJ"
   },
   "source": [
    "# 14.0 Saving the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "46CV19RlzZxL"
   },
   "source": [
    "We have now finished the experiment by finalizing the `tuned_lightgbm` model which is now stored in `final_lightgbm` variable. We have also used the model stored in `final_lightgbm` to predict `data_unseen`. This brings us to the end of our experiment, but one question is still to be asked: What happens when you have more new data to predict? Do you have to go through the entire experiment again? The answer is no, PyCaret's inbuilt function `save_model()` allows you to save the model along with entire transformation pipeline for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:07.971771Z",
     "start_time": "2022-07-17T04:28:07.750968Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "tXl6hkG9zZxN",
    "outputId": "4af19b19-6c0c-4b1e-a4f7-249e729091bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Successfully Saved\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<catboost.core.CatBoostRegressor at 0x7f9f2cb92eb0>,\n",
       " 'Final CatBoost Model.pkl')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_model(final_catboost, \"Final CatBoost Model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9LsyznpCzZxb"
   },
   "source": [
    "# 15.0 Loading the Saved Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7ZH-4EMLzZxd"
   },
   "source": [
    "To load a saved model at a future date in the same or an alternative environment, we would use PyCaret's `load_model()` function and then easily apply the saved model on new unseen data for prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:08.010701Z",
     "start_time": "2022-07-17T04:28:07.979769Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "2hsqdgn3zZxg",
    "outputId": "9db6b97b-ea93-452b-e4b4-121bea203bd3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Successfully Loaded\n"
     ]
    }
   ],
   "source": [
    "saved_final_catboost = load_model(\"Final CatBoost Model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NBAXt62nzZx5"
   },
   "source": [
    "Once the model is loaded in the environment, you can simply use it to predict on any new data using the same `predict_model()` function. Below we have applied the loaded model to predict the same `data_unseen` that we used in section 13 above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:08.287977Z",
     "start_time": "2022-07-17T04:28:08.015695Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "y7debJpCzZx8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_prediction = predict_model(saved_final_catboost, data=data_unseen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-17T04:28:08.305928Z",
     "start_time": "2022-07-17T04:28:08.289942Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "8hT1v3N0zZyD",
    "outputId": "c5f9994b-1859-49d0-9bb0-4d7f6c506b98"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "      <th>petroMag_u</th>\n",
       "      <th>petroMag_g</th>\n",
       "      <th>petroMag_r</th>\n",
       "      <th>petroMag_i</th>\n",
       "      <th>petroMag_z</th>\n",
       "      <th>logMstar_S16</th>\n",
       "      <th>logSFR_SED_S16</th>\n",
       "      <th>prediction_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.026444</td>\n",
       "      <td>17.287437</td>\n",
       "      <td>15.680633</td>\n",
       "      <td>14.896384</td>\n",
       "      <td>14.504523</td>\n",
       "      <td>14.259768</td>\n",
       "      <td>10.440</td>\n",
       "      <td>-2.474</td>\n",
       "      <td>9.007159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.026444</td>\n",
       "      <td>17.639717</td>\n",
       "      <td>15.913450</td>\n",
       "      <td>15.068103</td>\n",
       "      <td>14.646886</td>\n",
       "      <td>14.366766</td>\n",
       "      <td>10.480</td>\n",
       "      <td>-0.537</td>\n",
       "      <td>9.078650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.026442</td>\n",
       "      <td>17.498098</td>\n",
       "      <td>15.600836</td>\n",
       "      <td>14.838774</td>\n",
       "      <td>14.425499</td>\n",
       "      <td>14.211129</td>\n",
       "      <td>10.457</td>\n",
       "      <td>-0.812</td>\n",
       "      <td>8.984698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.026439</td>\n",
       "      <td>17.077347</td>\n",
       "      <td>15.375058</td>\n",
       "      <td>14.623333</td>\n",
       "      <td>14.252543</td>\n",
       "      <td>13.963070</td>\n",
       "      <td>10.546</td>\n",
       "      <td>-2.747</td>\n",
       "      <td>9.041953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.026436</td>\n",
       "      <td>17.146229</td>\n",
       "      <td>15.888835</td>\n",
       "      <td>15.313497</td>\n",
       "      <td>14.995555</td>\n",
       "      <td>14.815013</td>\n",
       "      <td>10.183</td>\n",
       "      <td>-0.171</td>\n",
       "      <td>9.015403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Z  petroMag_u  petroMag_g  petroMag_r  petroMag_i  petroMag_z  \\\n",
       "0  0.026444   17.287437   15.680633   14.896384   14.504523   14.259768   \n",
       "1  0.026444   17.639717   15.913450   15.068103   14.646886   14.366766   \n",
       "2  0.026442   17.498098   15.600836   14.838774   14.425499   14.211129   \n",
       "3  0.026439   17.077347   15.375058   14.623333   14.252543   13.963070   \n",
       "4  0.026436   17.146229   15.888835   15.313497   14.995555   14.815013   \n",
       "\n",
       "   logMstar_S16  logSFR_SED_S16  prediction_label  \n",
       "0        10.440          -2.474          9.007159  \n",
       "1        10.480          -0.537          9.078650  \n",
       "2        10.457          -0.812          8.984698  \n",
       "3        10.546          -2.747          9.041953  \n",
       "4        10.183          -0.171          9.015403  "
      ]
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xHqY/gC3BpqZXgOh67efNfBF3SgqvtehJsj+I37KRtsSha2S4dDKSHGT36oSs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+W/+wMNqzYSWF9PzHaMm9e362u2Ri+lSneG8LqNPU9/TZNFD/6LI+/9WZev18/6tItBB4cZvG1l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F+Zz/6F3ofVAWv3D5jSx6+OmuYiLDpHbxp9wSHM1Vn73DWQ/+jjPvv52P7nuU1S/P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\ncJIIqlZ/jN16SUG03E1LgWMrZAdZqCkgZEjMSIR0j0GeR2K28sZ2HMVLD/6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        "metadata": {
         "tags": []
        },
        "output_type": "display_data",
        "text/plain": "<Figure size 576x396 with 2 Axes>"
       }
      ]
     }
    },
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     "model_module": "@jupyter-widgets/controls",
     "model_name": "IntProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
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      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "",
      "description": "Processing: ",
      "description_tooltip": null,
      "layout": "IPY_MODEL_baf068abf86f44a9a9502f79dfe17eb5",
      "max": 5,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_14244d0fca4f40a8b3552675869748a3",
      "value": 4
     }
    },
    "5eb7949ac4a140118e9ee6be49921284": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "IntProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
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      "_view_count": null,
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      "_view_name": "ProgressView",
      "bar_style": "",
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