Ask many people the same question, then average their answers

Ask many people the same question, then average their answers

• A. Boosting
• B. Bagging
• C. Stacking
• D. Ensemble Techniques

Answer: (B)

Bagging, short for bootstrap aggregating, is about building many models on different samples of the data and then combining their predictions. The phrase “ask many people the same question, then average their answers” fits this approach perfectly: each model sees a slightly different dataset (a bootstrap sample), makes its prediction, and the final result is the average of all those predictions. This averaging reduces variance, making the overall estimate more stable and less sensitive to noise in any single dataset. In practice you generate many bootstrap samples from the training data, train a model on each sample, and then average the predictions for regression (or use a majority vote for classification). This is distinct from boosting, which trains models sequentially to fix previous errors; stacking combines outputs using a meta-model; and ensemble techniques is a broader category that includes bagging and others.

Bagging, short for bootstrap aggregating, is about building many models on different samples of the data and then combining their predictions. The phrase “ask many people the same question, then average their answers” fits this approach perfectly: each model sees a slightly different dataset (a bootstrap sample), makes its prediction, and the final result is the average of all those predictions. This averaging reduces variance, making the overall estimate more stable and less sensitive to noise in any single dataset.

In practice you generate many bootstrap samples from the training data, train a model on each sample, and then average the predictions for regression (or use a majority vote for classification). This is distinct from boosting, which trains models sequentially to fix previous errors; stacking combines outputs using a meta-model; and ensemble techniques is a broader category that includes bagging and others.