In this class I explain how the machine learning models decision and regression trees work. I explain the math behind how the models are built. I use Scikit-Learn models as examples. Finally I start to discuss hyperparameter tuning for decision trees.

I will continue with discussion of decision tree hyperparameter tuning as well as Random Forest and Gradient Boosted Trees in the Tree-Based Machine Learning Models, Part 2 class.

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Data Available Here and Code Available Here

Suggested Reading:

• Machine Learning with R, 3rd Edition, Chapter 5 (p. 125-165)
• Python Machine Learning, 3rd Edition, Chapter (p. 90-100)
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, Chapter 6 (p. 175-186)
• Impurity & Judging Splits — How a Decision Tree Works