What is the impact of applying bagging in constructing a decision tree model?

In the context of bagging (Bootstrap Aggregating), the primary purpose is to improve the stability and accuracy of machine learning algorithms, particularly decision trees. Bagging accomplishes this by creating multiple subsets of the original dataset through sampling with replacement. Each subset is used to train an individual decision tree, and the final prediction is made by aggregating the predictions from all the trees, usually by averaging in regression or by majority voting in classification.

The correct choice relating to the impact of applying bagging in constructing a decision tree model is that it reduces variance. Decision trees are known to be highly sensitive to the specific data on which they are trained, which can lead to high variance and overfitting. When using bagging, since multiple trees are built from different samples of the data, their individual errors can cancel each other out when aggregated, leading to a more robust overall model that is less dependent on any single set of data.

The other options, while relevant discussions in the context of model training and evaluation, do not accurately describe the primary impact of bagging. For instance, interpretability refers more to a model's transparency to human understanding, which is generally at odds with adding complexity through ensemble methods like bagging. Overfitting is actually what bag