IBM Data Science Practice Test 2026 – Comprehensive Exam Prep

In a random forest model, "bagging" specifically refers to the concept of bootstrap aggregating, which is the process of creating multiple samples from the training data and fitting multiple decision trees to these samples. This method enhances the robustness and accuracy of the model by reducing variance.

The "bootstrap" part involves randomly sampling the data with replacement to create different subsets for training each tree. Because each tree is trained on a slightly different subset of the data, the random forest can aggregate the predictions of all these trees, leading to a more reliable overall prediction. The aggregating aspect means that for a classification problem, the final prediction is determined by a majority vote across all the trees; for regression, it is typically the average of the predictions from all the trees.

This process effectively mitigates the chances of overfitting that might occur if a single model were trained on the entire dataset, as it promotes diversity among the models. Thus, the correct answer highlights the fundamental mechanism behind the random forest technique, which is crucial for its performance and effectiveness in various data science applications.