This article is about decision trees in machine learning. The figures under the leaves show the probability of survival and the percentage decision tree algorithm in data mining pdf observations in the leaf. Decision tree learning is a method commonly used in data mining. The goal is to create a model that predicts the value of a target variable based on several input variables.
An example is shown in the diagram at right. Each leaf represents a value of the target variable given the values of the input variables represented by the path from the root to the leaf. A decision tree is a simple representation for classifying examples. The arcs coming from a node labeled with an input feature are labeled with each of the possible values of the target or output feature or the arc leads to a subordinate decision node on a different input feature. Left: A partitioned two-dimensional feature space. These partitions could not have resulted from recursive binary splitting.