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Understanding Decision Trees

Durgesh Gupta provides a primer on the humble decision tree:

A decision tree is a graphical representation of all possible solutions to a decision.

The objective of using a Decision Tree is to create a training model that can use to predict the class or value of the target variable by learning simple decision rules inferred from training data.

It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.

The way I like to describe decision trees, especially to developers, is that a tree is a set of if-else statements which leads to a conclusion. The nice part about decision trees is that once you understand how they work, you’re halfway there to gradient boosting (e.g., XGBoost) and random forests.