Decision Boundaries

Partitioning training data

The concept of decision boundaries is vital to engineering the best features for the CART algorithm. A decision boundary is a geometry of how a machine learning model partitions the training data to produce a prediction. The best features allow the machine learning algorithms to partition the training data into the most effective decision boundaries.

Understanding decision boundaries is most easily accomplished via examples. The following code trains a decision tree to predict Survived from the combination of Sex, Pclass, and Embarked features. Run the code and examine the tree visualization.