CatBoost › Visualizing CatBoost

Michael BrenndoerferJuly 9, 20253 min read
Machine Learning from ScratchAll chapters
Part 5 of 9Tree-Based Models
Chapter 46CatBoost › Visualizing CatBoost
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Let's examine how CatBoost's symmetric tree structure compares to traditional decision trees and how its categorical feature processing works.

Out[6]:
Visualization
Bar chart showing feature importance with one-hot encoding.
Feature importance using traditional one-hot encoding approach. One-hot encoding creates multiple binary features for each categorical variable, leading to high dimensionality. The plot shows how features are split into separate binary indicators, which can make interpretation more difficult and increase the risk of overfitting.
Bar chart showing feature importance with CatBoost target encoding.
Feature importance using CatBoost-style target encoding approach. Target encoding maintains the original feature structure by replacing categories with numerical values based on their relationship with the target variable. This reduces dimensionality while preserving meaningful information about each feature's predictive power.

The visualization above shows the key difference between traditional categorical feature processing and CatBoost's approach. The left plot shows how one-hot encoding creates many binary features (leading to high dimensionality), while the right plot shows how CatBoost's target encoding approach maintains the original feature structure while learning optimal representations.