Decision Boundaries
Explore how decision boundaries define partitions in training data to enhance the performance of CART decision trees. Understand how categorical and numeric features create these boundaries, enabling you to visualize and select effective features for accurate model predictions.
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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 ...