Explain RF optimization and variable-importance pitfalls

Q: Explain RF optimization and variable-importance pitfalls

This question evaluates understanding of Random Forest regularization and feature-importance diagnostics, including recognition of biases between mean decrease impurity and permutation importance and considerations for reliable importance estimation and efficient training on large tabular datasets.

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Question

Optimize and Regularize a Random Forest Regressor for Tabular Data

Context: You are training a Random Forest (RF) regressor on tabular data and need to both regularize the model and interpret feature importance reliably, while keeping training efficient on large datasets.

Explain the following:

Why classic RFs do not prune trees post-training, and how max_depth, min_samples_leaf, and max_features control overfitting.
Two importance measures—mean decrease impurity (MDI) vs. permutation importance (PI): how each is computed, when they disagree, and biases (e.g., favoring high-cardinality categoricals or correlated features).
How to obtain reliable importances using out-of-bag (OOB) estimates, repeated permutations, or conditional/permutation schemes that account for correlations.
Practical steps to speed training on large data (e.g., subsampling, feature bagging, warm-starting trees).

Explain RF optimization and variable-importance pitfalls

Optimize and Regularize a Random Forest Regressor for Tabular Data

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Explain RF optimization and variable-importance pitfalls

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Optimize and Regularize a Random Forest Regressor for Tabular Data

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