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1. What is the main idea behind ensemble learning?

A. Combining multiple models to improve overall prediction accuracy

B. Using only one powerful model

C. Reducing the dataset size

D. Simplifying feature engineering

2. Which method involves combining classifiers by taking the majority vote?

A. Bagging

B. Majority voting

C. Gradient boosting

D. Adaptive boosting

3. What is a bootstrap sample in the context of bagging?

A. A random subset of data with replacement

B. A randomly selected feature subset

C. The original training dataset without changes

D. A validation dataset