Which type of decision tree predicts a categorical outcome?

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Multiple Choice

Which type of decision tree predicts a categorical outcome?

In decision trees, you choose between classification and regression based on the type of outcome you’re predicting. If the goal is to assign data into categories, the tree is built to produce class labels at its leaves, using splits that maximize the separation of classes (often via impurity measures like Gini or entropy). This is what a classification tree does. In contrast, regression trees predict continuous numeric values at the leaves, optimizing splits to minimize prediction error such as variance or mean squared error. While methods like Random Forest or Gradient Boosting can use trees for either classification or regression, the type specifically designed to predict a categorical outcome is the classification tree.

In decision trees, you choose between classification and regression based on the type of outcome you’re predicting. If the goal is to assign data into categories, the tree is built to produce class labels at its leaves, using splits that maximize the separation of classes (often via impurity measures like Gini or entropy). This is what a classification tree does. In contrast, regression trees predict continuous numeric values at the leaves, optimizing splits to minimize prediction error such as variance or mean squared error. While methods like Random Forest or Gradient Boosting can use trees for either classification or regression, the type specifically designed to predict a categorical outcome is the classification tree.

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