Decision tree learning

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. A classification or regression decision tree serves as a predictive model that draws conclusions about a set of observations. Where the target variable takes a discrete set of values the model is a classification tree, whose leaves represent class labels and whose branches represent conjunctions of features leading to those labels. Where the target variable takes continuous values, typically real numbers, the model is a regression tree, and the idea extends to any kind of object equipped with pairwise dissimilarities, such as categorical sequences. Decision trees are among the most popular machine learning algorithms because they are intelligible and simple: the resulting models are easy to interpret and visualize, even for users without a statistical background.