The purpose of this article is comparison between categorical encoding strategies. This guide aims to assist you picking the right strategy based on your application.
This post will discuss the different strategies to encode the categorical variables as a preprocessing step necessary to develop reliable machine learning models. Encode categorical variables is considered one of several actions that could enhance the model performance if applied appropriately. There are different encoding types; in this article, I picked the widely used encoders to discuss their pros and cons, and when it is appropriate to use as possible as I can. So, let’s get started.
Disclaimer: I attached an executable notebook for each encoding strategy hosted on kaggle. Each notebook has implementation in both pandas and sklearn, please feel free to run and report if you catch any bug or error.
In the end, I used sklearn to apply each strategy on the same dataset. After that, I utilized RandomForest and Logistic regression classifiers to compare each classifier’s performance based on the area under the curve — AUC.
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