Model Selection and Evaluation

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1. Which explanation matches random forests: combine different trees?A. Small decision trees are common building blocks.B. Ridge regression penalizes squared coefficient sizes, while lasso uses an absolute-value penalty that can set some coefficients to zero.C. The benefit comes from combining predictors whose errors are not perfectly correlated.D. A confusion matrix separates true positives, false positives, true negatives, and false negatives.

2. Which explanation matches boosting: improve a model in stages?A. The benefit comes from combining predictors whose errors are not perfectly correlated.B. Small decision trees are common building blocks.C. Ridge regression penalizes squared coefficient sizes, while lasso uses an absolute-value penalty that can set some coefficients to zero.D. A confusion matrix separates true positives, false positives, true negatives, and false negatives.

3. Which explanation matches regularization and cross-validation?A. Ridge regression penalizes squared coefficient sizes, while lasso uses an absolute-value penalty that can set some coefficients to zero.B. The benefit comes from combining predictors whose errors are not perfectly correlated.C. Small decision trees are common building blocks.D. A confusion matrix separates true positives, false positives, true negatives, and false negatives.

4. What should you do if training loss improves for many rounds while validation loss steadily worsens?A. Their errors may be strongly correlated, leaving little independent variation for averaging to reduce.B. Consider variation across folds, simplicity, stability, computational cost, and the application's error requirements rather than assuming a tiny average difference is decisive.C. Higher recall may prevent costly misses, but the number of false alarms must remain manageable for inspection staff and production.D. Investigate overfitting, use an earlier validated stopping point, and consider reducing complexity or strengthening regularization.

5. Why might a factory accept lower precision when missed defects are expensive, and what practical limit must it still consider?A. Their errors may be strongly correlated, leaving little independent variation for averaging to reduce.B. Investigate overfitting, use an earlier validated stopping point, and consider reducing complexity or strengthening regularization.C. Higher recall may prevent costly misses, but the number of false alarms must remain manageable for inspection staff and production.D. Consider variation across folds, simplicity, stability, computational cost, and the application's error requirements rather than assuming a tiny average difference is decisive.

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Model Selection and Evaluation

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