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+> * 原文地址：[Why One-Hot Encode Data in Machine Learning](https://machinelearningmastery.com/why-one-hot-encode-data-in-machine-learning/)
+> * 原文作者：[Jason Brownlee](https://machinelearningmastery.com/author/jasonb/
+> * 译文出自：[掘金翻译计划](https://github.com/xitu/gold-miner)
+> * 本文永久链接：[https://github.com/xitu/gold-miner/blob/master/TODO1/why-one-hot-encode-data-in-machine-learning.md.md](https://github.com/xitu/gold-miner/blob/master/TODO1/why-one-hot-encode-data-in-machine-learning.md.md)
+> * 译者：
+> * 校对者：
+
+# Why One-Hot Encode Data in Machine Learning?
+
+Getting started in applied machine learning can be difficult, especially when working with real-world data.
+
+Often, machine learning tutorials will recommend or require that you prepare your data in specific ways before fitting a machine learning model.
+
+One good example is to use a one-hot encoding on categorical data.
+
+*   Why is a one-hot encoding required?
+*   Why can’t you fit a model on your data directly?
+
+In this post, you will discover the answer to these important questions and better understand data preparation in general in applied machine learning.
+
+Let’s get started.
+
+![Why One-Hot Encode Data in Machine Learning?](https://3qeqpr26caki16dnhd19sv6by6v-wpengine.netdna-ssl.com/wp-content/uploads/2017/07/Why-One-Hot-Encode-Data-in-Machine-Learning.jpg)
+
+Why One-Hot Encode Data in Machine Learning?
+Photo by [Karan Jain](https://www.flickr.com/photos/jiangkeren/8263176332/), some rights reserved.
+
+## What is Categorical Data?
+
+Categorical data are variables that contain label values rather than numeric values.
+
+The number of possible values is often limited to a fixed set.
+
+Categorical variables are often called [nominal](https://en.wikipedia.org/wiki/Nominal_category).
+
+Some examples include:
+
+*   A “_pet_” variable with the values: “_dog_” and “_cat_”.
+*   A “_color_” variable with the values: “_red_”, “_green_” and “_blue_”.
+*   A “_place_” variable with the values: “first”, “_second_” _and_ “_third_”.
+
+Each value represents a different category.
+
+Some categories may have a natural relationship to each other, such as a natural ordering.
+
+The “_place_” variable above does have a natural ordering of values. This type of categorical variable is called an ordinal variable.
+
+## What is the Problem with Categorical Data?
+
+Some algorithms can work with categorical data directly.
+
+For example, a decision tree can be learned directly from categorical data with no data transform required (this depends on the specific implementation).
+
+Many machine learning algorithms cannot operate on label data directly. They require all input variables and output variables to be numeric.
+
+In general, this is mostly a constraint of the efficient implementation of machine learning algorithms rather than hard limitations on the algorithms themselves.
+
+This means that categorical data must be converted to a numerical form. If the categorical variable is an output variable, you may also want to convert predictions by the model back into a categorical form in order to present them or use them in some application.
+
+## How to Convert Categorical Data to Numerical Data?
+
+This involves two steps:
+
+1.  Integer Encoding
+2.  One-Hot Encoding
+
+### 1. Integer Encoding
+
+As a first step, each unique category value is assigned an integer value.
+
+For example, “_red_” is 1, “_green_” is 2, and “_blue_” is 3.
+
+This is called a label encoding or an integer encoding and is easily reversible.
+
+For some variables, this may be enough.
+
+The integer values have a natural ordered relationship between each other and machine learning algorithms may be able to understand and harness this relationship.
+
+For example, ordinal variables like the “place” example above would be a good example where a label encoding would be sufficient.
+
+### 2. One-Hot Encoding
+
+For categorical variables where no such ordinal relationship exists, the integer encoding is not enough.
+
+In fact, using this encoding and allowing the model to assume a natural ordering between categories may result in poor performance or unexpected results (predictions halfway between categories).
+
+In this case, a one-hot encoding can be applied to the integer representation. This is where the integer encoded variable is removed and a new binary variable is added for each unique integer value.
+
+In the “_color_” variable example, there are 3 categories and therefore 3 binary variables are needed. A “1” value is placed in the binary variable for the color and “0” values for the other colors.
+
+For example:
+
+```
+red, green, blue
+1, 0, 0
+0, 1, 0
+0, 0, 1
+```
+The binary variables are often called “dummy variables” in other fields, such as statistics.
+
+## One Hot Encoding Tutorials
+
+
+Looking for some tutorials on how to one hot encode your data in Python, see:
+
+*   [Data Preparation for Gradient Boosting with XGBoost in Python](https://machinelearningmastery.com/data-preparation-gradient-boosting-xgboost-python/)
+*   [How to One Hot Encode Sequence Data in Python](https://machinelearningmastery.com/how-to-one-hot-encode-sequence-data-in-python/)
+
+## Further Reading
+
+*   [Categorical variable](https://en.wikipedia.org/wiki/Categorical_variable) on Wikipedia
+*   [Nominal category](https://en.wikipedia.org/wiki/Nominal_category) on Wikipedia
+*   [Dummy variable](https://en.wikipedia.org/wiki/Dummy_variable_(statistics)) on Wikipedia
+
+## Summary
+
+In this post, you discovered why categorical data often must be encoded when working with machine learning algorithms.
+
+Specifically:
+
+*   That categorical data is defined as variables with a finite set of label values.
+*   That most machine learning algorithms require numerical input and output variables.
+*   That an integer and one hot encoding is used to convert categorical data to integer data.
+
+Do you have any questions?
+
+Post your questions to comments below and I will do my best to answer.
+
+> 如果发现译文存在错误或其他需要改进的地方，欢迎到 [掘金翻译计划](https://github.com/xitu/gold-miner) 对译文进行修改并 PR，也可获得相应奖励积分。文章开头的 **本文永久链接** 即为本文在 GitHub 上的 MarkDown 链接。
+
+---
+
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