Summary

This week, we will introduce another nonparametric method for supervised learning: decision trees.

Learning Objectives

After completing this week, you are expected to be able to:

  • Understand how decision trees differ from KNN when determining similarity of data.
  • Find and evaluate decision tree splits for regression.
  • Find and evaluate decision tree splits for classification.
  • Use decision trees to make predictions for regression tasks using sklearn.
  • Use decision trees to make predictions for classification tasks using sklearn.
  • Use decision trees to estimate conditional probabilities for classification tasks using sklearn.
  • Tune the parameters of decision trees to avoid overfitting.

Topics

  • Regression Trees
  • Classification Trees

Activities