Q11easyWhich of the following is a classic Boosting algorithm?AAdaBoostBK-MeansCBagging RegressorDDecision TreeCheck AnswerReveal Answer & ExplanationDiscuss on Board
Q12easyA developer sets n_estimators=100 in a Random Forest. What does this indicate?AThe model will train for 100 hours.BThe trees will have a maximum depth of 100.CThe ensemble will build exactly 100 decision trees.DThe model will use 100 features.Check AnswerReveal Answer & ExplanationDiscuss on Board
Q13easyWhat is the main characteristic of Gradient Boosting?AIt uses a voting system at the end.BIt is an unsupervised learning technique.CIt trains subsequent models to predict the residuals (errors) of the preceding models.DIt ignores all errors.Check AnswerReveal Answer & ExplanationDiscuss on Board
Q14easyWhat is Feature Randomness in Random Forests?ARandomly deleting features to speed up training.BUsing random data for the target.CAt each split in a tree, the algorithm considers only a random subset of features.DShuffling the order of columns in the CSV.Check AnswerReveal Answer & ExplanationDiscuss on Board
Q15easyWhich of the following is an advantage of Boosting over Bagging?AIt can often achieve higher accuracy by focusing on hard-to-classify samples.BIt is always faster to train.CIt is less likely to overfit.DIt can be trained on a single CPU core much better.Check AnswerReveal Answer & ExplanationDiscuss on Board