Which type of decision tree predicts a continuous outcome?

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Multiple Choice

Which type of decision tree predicts a continuous outcome?

Predicting a numeric, continuous value is the job of a regression tree. It splits the data to create regions where the target variable is as similar as possible, typically by minimizing variance (or mean squared error) within each region. At each leaf, the prediction is the average of the target values from the training samples that reach that leaf, yielding a numeric output rather than a category. This differs from classification trees, which aim to assign category labels and split to separate classes. Ensemble methods like random forests or gradient boosting are collections of trees and can be used for regression, but the fundamental type of tree that directly outputs a continuous prediction is a regression tree.

Predicting a numeric, continuous value is the job of a regression tree. It splits the data to create regions where the target variable is as similar as possible, typically by minimizing variance (or mean squared error) within each region. At each leaf, the prediction is the average of the target values from the training samples that reach that leaf, yielding a numeric output rather than a category.

This differs from classification trees, which aim to assign category labels and split to separate classes. Ensemble methods like random forests or gradient boosting are collections of trees and can be used for regression, but the fundamental type of tree that directly outputs a continuous prediction is a regression tree.

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