Module 5c: Machine Learning — Tree Methods

Resources

• Slides: [Supervised ML] Trees and Forests
• Notebook (GitHub)
• Exercises (GitHub)

Learning objectives

After this lesson, you should be able to:

• Understand the difference between
  • supervised and unsupervised learning
  • classification and regression
• Explain the following concepts (and why they are important):
  • training and test sets
  • overfitting
  • bias-variance tradeoff
  • cross-validation
  • hyperparameter tuning
• Use the following ML algorithms:
  • linear regression
  • logistic regression
  • decision trees
  • random forests
  • k-nearest neighbors
• Use the scikit-learn library to train, select and use a model

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