Virtual Labs

Random Forest

Which technique is used to improve Random Forest stability?

a: Pruning Explanation

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b: Normalization Explanation

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c: Standardization Explanation

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d: Bagging Explanation

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Which is a key practical advantage of Random Forest over a single Decision Tree?

a: Usually lower variance and better generalization Explanation

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b: Always easier to interpret Explanation

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c: Needs strict feature scaling Explanation

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d: Uses only one tree for prediction Explanation

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What happens to bias and variance in Random Forest compared to a single tree?

a: Bias increases, variance increases Explanation

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b: Bias decreases, variance decreases Explanation

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c: Bias increases, variance decreases Explanation

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d: Bias remains constant, variance increases Explanation

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Which Random Forest parameter controls feature randomness at each split?

a: n_estimators Explanation

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b: max_features Explanation

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c: min_samples_split Explanation

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d: max_depth Explanation

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Which output aggregation method is used in Random Forest regression?

a: Majority voting Explanation

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b: Median value Explanation

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c: Mean of predictions Explanation

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d: Gradient update Explanation

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What information can Random Forest provide besides predictions?

a: Gradient values Explanation

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b: Feature importance scores Explanation

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c: Cluster assignments Explanation

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d: Reinforcement rewards Explanation

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What does the Out-of-Bag (OOB) score in Random Forest estimate?

a: Training accuracy on bootstrap samples Explanation

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b: Model complexity Explanation

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c: Generalization performance using samples not seen by each tree Explanation

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d: Number of features selected at each split Explanation

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In Random Forest, increasing n_estimators from 50 to 300 usually has what effect?

a: Often improves stability/accuracy but with diminishing returns and more computation Explanation

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b: Always causes severe overfitting Explanation

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c: Reduces model size and training time Explanation

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d: Disables bagging Explanation

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How does reducing max_features typically influence a Random Forest?

a: It can increase tree diversity and reduce correlation among trees Explanation

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b: It removes the need for bootstrap sampling Explanation

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c: It guarantees higher accuracy for every dataset Explanation

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d: It converts Random Forest into boosting Explanation

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What does a high feature importance value indicate in a trained Random Forest?

a: That feature contributes strongly to impurity reduction across trees Explanation

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b: That feature should always be removed Explanation

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c: That feature was never used in splits Explanation

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d: That feature is categorical Explanation

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