Introduction

Decision tree learning: Decision trees are very popular tools for classification and prediction problems. A decision tree is a classifier which recursively partitions the instance space or the variable set. Decision trees are represented as a tree structure where each node can be classified as either a leaf node or a decision node. A leaf node holds the value of the target attribute, while a decision node specifies the rule to be implemented on a single attribute-value. Each decision node splits the instance space into two or more sub-spaces according to a certain discrete function of the input attributes-values. Each test considers a single attribute, such that the instance space is partitioned according to the attribute's value. In the case of numeric attributes, the condition refers to a range. After implementing the rule on the decision node, a sub-tree is an outcome. Each of the leaf nodes holds a probability vector indicating the probability of the target attribute having...