MACHINE LEARNING

Detailed explanation-1: -For each split, individually calculate the entropy of each child node. Calculate the entropy of each split as the weighted average entropy of child nodes. Select the split with the lowest entropy or highest information gain.

Detailed explanation-2: -Splitting: Splitting is the process of dividing the decision node/root node into sub-nodes according to the given conditions.

Detailed explanation-3: -The Root Node: Is the node that starts the graph. In a normal decision tree it evaluates the variable that best splits the data. Intermediate nodes: These are nodes where variables are evaluated but which are not the final nodes where predictions are made.

Detailed explanation-4: -The process of splitting a single node into many nodes is known as splitting. A leaf node, also known as a terminal node, is a node that does not break into other nodes. A branch, sometimes known as a sub-tree, is a section of a decision tree.