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Good Question !

decision trees use multiple algorithms to decide to split a node in two or more sub-nodes,in other words, we can say that purity of the node increases with respect to the target variable. Decision tree splits the nodes on all available variables and then selects the split which results in most homogeneous

A tree is built by dividing the source set, constituting the root node of the tree, into subsets - which constitute the successor children. Splitting is based on a set of splitting rules based on classification characteristics. This process is repeated on each derived subset in a recursive manner called recursive partitioning. Recursion is complete when the subset at the node level all has the same values ​​of the target variable, or when the splitting no longer adds any value to the predictions.