Decision Trees and Ordinal Encoding: A Practical Guide
Categorical variables are pivotal as they often carry essential information that influences the outcome of predictive models. However, their non-numeric nature presents unique challenges in model processing, necessitating specific strategies for encoding. This post will begin by discussing the different types of categorical data often encountered in datasets. We will explore ordinal encoding in-depth and how it can be leveraged when implementing a Decision Tree Regressor. Through practical Python examples using the OrdinalEncoder from sklearn and the Ames Housing dataset, this guide will provide you with the skills to implement these strategies effectively. Additionally, we will visually demonstrate how these encoded variables influence the decisions of a Decision Tree Regressor.
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