Compare Random Forests and Boosted Trees: Bias, Variance, Speed

Q: Compare Random Forests and Boosted Trees: Bias, Variance, Speed

This question evaluates understanding of ensemble machine learning methods—specifically differences between Random Forests and gradient-boosted decision trees—and the impact of feature preprocessing on tree-based models.

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Question

Scenario

A product/data science team is deciding between Random Forests and Gradient-Boosted Decision Trees (e.g., XGBoost) for a new predictive task. They also want to know whether they must standardize or normalize features for these tree-based models.

Task

Compare Random Forests and Gradient-Boosted Decision Trees in terms of:

Bias vs. variance
Interpretability
Training speed and inference speed
Robustness to overfitting

Then answer: Is feature standardization or normalization necessary for tree-based models? Explain why or why not.

Hints

Contrast how ensembles are constructed (bagging vs. boosting), including parallel vs. sequential learning.
Note how split criteria (e.g., Gini/entropy, squared error) work in trees.
Explain how trees are invariant to monotonic transformations of features (ordering-based splits).

Compare Random Forests and Boosted Trees: Bias, Variance, Speed

Scenario

Task

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Solution

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Compare Random Forests and Boosted Trees: Bias, Variance, Speed

Overview

Scenario

Task

Hints

Solution

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