Select all correct statements regarding decision trees and their

applications:

Decision trees typically use a method called recursive binary splitting to segment

the predictor space into regions, which are often high$-$dimensional rectangles,

boxes, and diagonal lines.

In regression trees, predictions for a given observation are made using the mean

response of the training observations within the same region.

Although decision trees are simple and useful for interpretation, they are typically

less accurate in prediction compared to advanced supervised learning methods,

such as ensemble techniques like random forests and boosting.

A classification tree predicts the response by assigning the mean to the most

commonly occurring class of training observations in the region to which it

belongs.

Decision trees can be applied to both regression and classification tasks, where

they segment the predictor space into a number of simple regions. Predictions for

an observation are based on the mean or mode of the responses in the region

where it belongs.