Decision tree - Entropy and Information gain with Example

#decisiontree #informationgain #decisiontreeentropy Decision tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node holds a class label.  Entropy is the measure of impurity in a dataset. Information gain is the measure of reduction in entropy. It is used to select the best splitting attribute when constructing a decision tree.