Ensembles

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1. Which explanation matches bootstrap samples and bagging?A. A random forest samples candidate features at splits as well as bootstrap records.B. Boosting trains models in sequence so later stages address errors under a chosen objective.C. Combining predictions helps most when their mistakes differ.D. Bagging uses bootstrap samples and aggregates model outputs to reduce some instability.

2. Which explanation matches random forest feature choices?A. Bagging uses bootstrap samples and aggregates model outputs to reduce some instability.B. Boosting trains models in sequence so later stages address errors under a chosen objective.C. A random forest samples candidate features at splits as well as bootstrap records.D. Combining predictions helps most when their mistakes differ.

3. Which explanation matches boosting as sequential correction?A. Bagging uses bootstrap samples and aggregates model outputs to reduce some instability.B. Boosting trains models in sequence so later stages address errors under a chosen objective.C. A random forest samples candidate features at splits as well as bootstrap records.D. Combining predictions helps most when their mistakes differ.

4. For random forest feature choices, which result or action fits this practical case: Every tree keeps choosing mileage first.A. Compare tree diversity when each split sees only a subset of inputs.B. Draw training records with replacement for each tree and compare voting with one tree.C. Add a small corrective tree, then check validation performance after each stage.D. Tabulate cases where every model is wrong, not only average ensemble accuracy.

5. For inspect shared errors in an ensemble, which result or action fits this practical case: All ensemble members miss the same rural route.A. Draw training records with replacement for each tree and compare voting with one tree.B. Compare tree diversity when each split sees only a subset of inputs.C. Add a small corrective tree, then check validation performance after each stage.D. Tabulate cases where every model is wrong, not only average ensemble accuracy.

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Ensembles

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