Which algorithm is often associated with building decision trees?

The algorithm most commonly associated with building decision trees is indeed ID3 or CART. These algorithms focus on creating a model that can make decisions based on features of the input data.

ID3 (Iterative Dichotomiser 3) builds a decision tree by selecting the feature that provides the highest information gain at each node. This selection process continues recursively, leading to a tree structure that represents the decision-making process based on the given features. CART (Classification and Regression Trees) is another widely used algorithm for decision tree creation that can handle both classification and regression tasks. It works by splitting the data based on features that minimize a cost function, thereby optimizing the structure of the tree to make the best predictive decision.

Both ID3 and CART rely on the principles of splitting data into subsets to create conditions for decision making, inherently using a tree topology, which is why they are directly linked to the concept of decision trees. This makes them the correct choice when asked about algorithms specifically used for this purpose. Other algorithms listed, like Support Vector Machine, K-Nearest Neighbors, and Naive Bayes, operate on different principles that do not involve the decision tree methodology.