What type of learning does a decision tree primarily fall under?

A decision tree primarily falls under supervised learning because this method involves training a model on a labeled dataset, where the outputs are known. In supervised learning, the algorithm learns a mapping from inputs to outputs based on the examples provided in the training data. A decision tree creates a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. Each decision node corresponds to a feature that splits the data into subsets based on certain conditions, ultimately leading to a final prediction, which represents the target variable.

In contrast, unsupervised learning involves training a model on input data without labeled responses, focusing instead on identifying patterns or groupings within the data. Reinforcement learning hinges on the idea of making decisions through trial and error, optimizing a reward function, rather than learning from labeled data. Deep learning, while it can use supervised learning techniques, specifically refers to neural network architectures and is a broader category that encompasses various learning strategies. Hence, decision trees distinctly operate under the framework of supervised learning.