Implement a decision tree : Working of a Decision Tree

The video tutorial explains how decision trees are split using criteria like the Gini index and information gain. It details the calculation of the Gini index to measure homogeneity and how information gain is used to reduce impurity by decreasing entropy. Examples are provided to illustrate these concepts, focusing on selecting criteria for splitting nodes based on attributes like college degree and years of experience.

What is the purpose of splitting the root node in a decision tree?

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What is the significance of the Gini index in decision tree algorithms?

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Explain how the Gini index is calculated for a node.

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Describe the concept of information gain in the context of decision trees.

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How does entropy relate to the impurity of a node in a decision tree?

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