Artificial Intelligence Programming 2025 – 400 Free Practice Questions to Pass the Exam

Ensemble learning is a method that combines multiple models to make predictions, which is why the second choice is the correct answer. The core idea behind ensemble learning is that by aggregating predictions from various models, the overall performance can often be improved compared to using a single model. This approach leverages the strength of diverse models to reduce the risk of overfitting and to increase the robustness of predictions.

The multiple models within an ensemble may vary in type, architecture, or parameters, and they work together to provide a more accurate and reliable final prediction. Popular ensemble methods include bagging, boosting, and stacking, which each employ different strategies to combine model outputs effectively.

Alternative answers do not capture the essence of ensemble learning. The first choice restricts the focus to single models, which contrasts with the collaborative nature of ensemble methods. The third choice deals with model pruning, which is a separate concept aimed at reducing model complexity by removing less informative features or parts of a model. Lastly, the fourth choice limits the discussion to decision trees, while ensemble learning is applicable to multiple model types, not just trees. This highlights the breadth and versatility of ensemble techniques in various machine learning contexts.