In machine learning, a decision tree is a predictive model; that is, a mapping of observations about an item to conclusions about the item's target value. Each inner node corresponds to variable; an arc to a child represents a possible value of that variable. A leaf represents the predicted value of target variable given the values of the variables represented by the path from the root. The machine learning technique for inducing a decision tree from data is called decision tree learning, or (colloquially) Decision Trees.
It is also a mean for calcuating conditional probabilities.