What is the primary advantage of ensemble methods in machine learning?

Correct answer: Combining multiple models to achieve better performance and robustness than individual models

Option A is correct because ensemble methods such as bagging, boosting, and stacking combine predictions from multiple base learners, which reduces both variance and bias relative to any single model and leads to more robust generalization on unseen data. Option B is incorrect because ensembles are typically harder to interpret than a single decision tree or linear model; they sacrifice interpretability for predictive power. Option C is incorrect because ensembles still require rigorous validation; in fact, hyperparameter tuning for ensemble size and combination strategy makes validation more important. Option D is incorrect because training and inferring with multiple models increases computational cost compared to a single model.