Understanding the structural formulation of decision trees and how it can be problematic at times.

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Decision trees often create perpendicular split conditions which can lead to overfitting, particularly with diagonal decision boundaries. Running PCA before fitting a decision tree can project data into orthogonal space, potentially reducing the tree's depth and improving performance. However, PCA components are not interpretable, which can be a limitation in some cases. Proper feature engineering might be necessary for better model performance.

How to Inspect Decision Trees After Training with PCA

For those who want to build a career in DS/ML on core expertise, not fleeting trends:

<p>I totally agree with using PCA to simplify decision trees, but in my experience, it’s also crucial to not overlook the importance of feature interpretability in certain domains - sometimes, the trade-off in model complexity isn’t worth losing the ability to understand what’s driving your predictions 🤔.</p>
