Decision tree - Entropy and Information gain with Example

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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.

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