Decision Tree - Algorithm & Demo | Part-5

A decision tree is a tree where each node represents a feature(attribute), each link(branch) represents a decision(rule) and each leaf represents an outcome(categorical or continues value).How to build this? We will use Entropy function and Information gain as metrics. Choose the attribute with the highest information gain. calculate the entropy and information gain for every attribute. Try to pick the highest gain attribute. Repeat the same thing for sub-trees till we get the tree. The procedure of creating a tree is discussed in detail. Watch the video to have a clear understanding of the concept of Decision Tree Algorithm.

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