Which of the following statements about decision trees is FALSE?

In the context of decision trees and ensemble methods like bagging and random forests, the statement that bagging reduces variance through pruning is false. Instead, bagging primarily reduces variance through the creation of multiple bootstrapped datasets and aggregating the predictions from various models built on these subsets. By averaging the outcomes of the ensemble of trees, bagging effectively diminishes overfitting, which is a major contributor to variance in model predictions.

Pruning, on the other hand, is a technique applied to individual decision trees to remove branches that have little importance, thereby simplifying the model and potentially reducing overfitting. However, in the case of bagging, the focus is on leveraging the aggregate predictions of numerous unpruned trees to stabilize the model rather than applying pruning to individual trees.

The other statements about decision trees and ensemble methods are true. Bagging inherently involves bootstrapping to create diverse datasets. Random forests, while they can use bootstrapping, do have the flexibility to operate even without it, although bootstrapping is commonly used to enhance the randomness and robustness of the model. Additionally, the processes utilized in random forests can indeed be represented using tree diagrams, as they consist of multiple decision trees, each contributing to the final prediction.