The DT is trained. Natural overfitting presents. Hyper-parameters get tuned (unsatisfactorily). Finally, the tree is replaced with Random Forest. While that may be a quick win for performance, the…

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The article explores the benefits of using decision trees in machine learning and how to improve their performance through Principal Component Analysis (PCA) and feature engineering. It emphasizes the importance of understanding and explaining results in industries where failures can pose significant risks. By applying PCA to decision tree models, better decision boundaries and more intuitive results can be achieved. The article provides a step-by-step guide for implementing PCA with decision trees and discusses the improvements in accuracy, precision, and recall achieved through this approach.

One Step to Make Decision Trees Produce Better Results

Background, implementation, and model improvement