Decision tree learning is a supervised learning approach used in
statistics, data mining and machine learning. In this formalism, a
classification or regression decision tree is used as a predictive model to
draw conclusions about a set of observations.
Tree models where the target variable can take a discrete set of values are
called classification trees ; in these tree structures, leaves represent
class labels and branches represent conjunctions of features that lead to
those class labels. Decision trees where the target variable can take
continuous values (typically real numbers) are called regression trees.