AutoCV

AutoCV is an automated cross-validation framework designed to simplify and streamline the process of cross-validation in machine learning projects. It aims to reduce the manual effort involved in evaluating machine learning models by providing an easy-to-use interface and a set of tools that automate various cross-validation tasks.

Features

• Automated Cross-Validation: Automates the process of performing cross-validation for different models and datasets.
• Support for Multiple Model Types: Compatible with various types of machine learning models.
• Default Performance Metrics: Provides built-in performance metrics for evaluating models.
• Extensible Framework: Easily extendable with custom metrics and validation techniques.

Installation

To install AutoCV, clone the repository and run the setup script:

git clone https://github.com/0xkerem/AutoCV.git
cd AutoCV
pip install -e .

Usage

Here's a brief example of how to use AutoCV with a LogisticRegression model:

from autocv import AutoCV
import numpy as np
from sklearn.linear_model import LogisticRegression

# Example dataset
X = np.random.rand(100, 10)  # 100 samples, 10 features
y = np.random.randint(0, 2, 100)  # Binary target variable

# Initialize the AutoCV object with a LogisticRegression model
model = LogisticRegression()
autocv = AutoCV(model=model)

# Perform cross-validation
results = autocv.cross_validate(X, y)
print(results)

Examples and Tests

The examples and tests directories will be updated with comprehensive examples and test cases in future contributions. Stay tuned for updates!

Contributing

We welcome contributions from the community. If you have any improvements or new features to add, feel free to open a pull request.

License

AutoCV is licensed under the Apache License, Version 2.0. You may obtain a copy of the license at http://www.apache.org/licenses/LICENSE-2.0.