Amazon·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Amazon ML Engineer screen, one meaty conceptual question about tree-based models. Pretty standard for this type of role but they pushed on the details more than I expected.

Questions Asked (1)

Compare decision trees and random forests. Walk through how each is built, and discuss the tradeoffs around bias, variance, overfitting, and interpretability.

Technical Trade-offsAlgorithms & Data Structures

Author's notes

I started with the single tree stuff which felt fine, splitting on features, stopping criteria, easy.

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AI HintsAI Generated

Suggested Approach

Start by defining decision trees and random forests, then walk through their construction processes. Compare them across bias, variance, overfitting, and interpretability, and conclude with practical trade-offs and when to use each.

Pro tip: Emphasize that random forests reduce variance through bagging and feature randomness, but at the cost of interpretability; mention that in practice, you can use feature importances or SHAP values to regain some interpretability.

1. Define Decision Trees

Explain that a decision tree is a flowchart-like model that splits data based on feature thresholds to maximize purity (e.g., Gini or entropy). Mention that it recursively partitions the feature space.

2. Define Random Forests

Describe random forests as an ensemble of decision trees trained on bootstrapped samples with random feature subsets at each split. The final prediction is aggregated (majority vote or average).

3. Compare Bias and Variance

Discuss that a single decision tree has low bias but high variance, while random forests reduce variance by averaging multiple trees, often maintaining low bias.

4. Discuss Overfitting

Explain that decision trees are prone to overfitting, especially if deep, while random forests are more robust due to ensemble averaging, though they can still overfit with noisy data.

5. Address Interpretability

Highlight that decision trees are highly interpretable (if-else rules), whereas random forests are less interpretable but can provide feature importance scores.

Key Points to Mention

Decision trees split data recursively to maximize information gain or Gini impurity reduction.
Random forests use bagging (bootstrap aggregating) and random feature selection to decorrelate trees.
Bias-variance tradeoff: single tree = low bias, high variance; random forest = low bias, reduced variance.
Overfitting: decision trees overfit easily; random forests mitigate but can overfit with noisy data.
Interpretability: decision trees are transparent; random forests are black-box but offer feature importances.
Practical considerations: random forests often outperform single trees but require more computation and memory.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.