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 primary goal when splitting nodes in a decision tree?

• To increase the number of nodes

• To achieve homogeneity

• To maximize the number of leaves

• To decrease the tree height

Which of the following is a method used to determine the best split in a decision tree?

• Leaf count

• Node height

• Gini index

• Random selection

In the example provided, what was the Gini index used to evaluate?

• The homogeneity of nodes based on college degree and experience

• The number of nodes

• The success of a marketing campaign

• The height of the decision tree

What does information gain aim to maximize in a decision tree?

• The number of nodes

• The impurity of nodes

• The homogeneity by reducing impurity

• The height of the tree

How is entropy used in calculating information gain?

• By counting the number of leaves

• By increasing the number of nodes

• By measuring the impurity of nodes

• By calculating the height of the tree