A. In Random Forest classification, bagging, or bootstrap aggregation, combines predictions from multiple decision trees to reduce variance and avoid overfitting. By using different subsets of the training data (via sklearn's RandomForestClassifier), it ensures that individual models generalize better. The model enhances its overall performance by making the final prediction based on a majority vote.