In this video, I cover the basics of Random Forest and Gradient Boosted Trees. First, I review hyperparameters of tree-based models used to tune these models. Then I cover the details of how Random Forest and Gradient Boosted Trees make accurate predictions. I then demo the models coded in Python scikit-learn.

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Data Available Here and Code Available Here

Suggested Reading:

• Machine Learning with R, 3rd Edition, Chapter 11, Improving Model Performance with Meta-Learning section (p. 359-374)
• Python Machine Learning, 3rd Edition, Chapter 7 (p. 223-257)
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, Chapter 6 (p. 189-211)