Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

Ensemble methods in machine learning are primarily designed to combine multiple models to enhance predictive performance. This approach takes advantage of the strengths of different algorithms or models, creating a system that often performs better than any individual model could on its own. By aggregating the predictions from multiple models, ensembles can reduce variance (in the case of bagging methods) or bias (for boosting methods), leading to more accurate and robust predictions.

For instance, a common ensemble technique like Random Forest combines several decision trees, where the final prediction is made based on the aggregated output from all the trees. This collective wisdom tends to mitigate errors that any single model might introduce.

While simplifying the model and reducing overfitting is a valid concern in machine learning processes, ensemble methods specifically focus on combining models rather than just simplifying them. Similarly, increasing the speed of model training or visualizing data in lower dimensions pertains to different aspects of machine learning and does not directly relate to the primary function of ensemble methods.