Artificial Intelligence Programming Practice Exam 2026 - Free AI Programming Practice Questions and Study Guide

Ensemble learning aims to deliver better predictions than individual models by combining the strengths of multiple models or algorithms. The underlying principle of ensemble methods is that different models can capture various aspects of the data, and by aggregating their predictions, the ensemble can achieve higher accuracy and robustness.

This approach often reduces the risk of overfitting, as the combined predictions tend to average out errors that individual models might make. For instance, if multiple models make predictions on the same data, the errors of some models may be balanced out by the correct predictions of others, leading to a more reliable overall output. Techniques such as bagging, boosting, and stacking are commonly employed in ensemble learning to enhance prediction performance.

Other options do not accurately reflect the goal of ensemble learning. While creating a single complex model could sometimes happen as a result of ensemble techniques, it is not the primary aim. Reducing the number of training cycles or increasing dependence on a single algorithm also does not align with the core objective of improving prediction accuracy through diversity of models.