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

Bagging, short for Bootstrap Aggregating, is a technique specifically designed to improve the stability and accuracy of machine learning algorithms. The correct statement emphasizes that bagging is a method to combine results from the same type of model using bootstrapping.

In this process, multiple subsets of the training data are created through random sampling with replacement, which is known as bootstrapping. Each of these subsets is then used to train a separate instance of the same model. The predictions from these individual models are combined—usually by averaging for regression tasks or by majority voting for classification tasks. This ensemble approach effectively reduces variance and helps to mitigate overfitting, leading to more robust predictions than any single model.

The other statements do not capture the core principles of bagging. For instance, merging predictions from multiple different models refers to a different ensemble technique, often called stacking or blending, rather than the uniform approach used in bagging which focuses on the same model type. Building a single complex model for prediction contradicts the foundational idea of bagging, which relies on averaging multiple simpler models instead. Finally, evaluating models using the same training set does not pertain specifically to bagging, which emphasizes training on varied subsets of data rather than a singular training set for