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Module 2: Decision Trees

In this module, we will introduce the decision tree model. We will explain the structure of decision trees and the process it take to make predictions.

0Module Learning Outcomes

1 Introducing Decision Trees

2Decision Tree - Trees

3Decision Tree Outcome

4Building a Decision Tree Classifier

5Predicting with a Decision Tree

6Decision Trees True/False

7Building a Decision Tree Classifier

8Decision Trees with Continuous Features

9Decision Boundaries

10Decision Boundaries

11Decision Boundaries

12Parameters and Hyperparameters

13Feature Splitting

14Parameter or Hyperparameter

15Playing with Hyperparameters

16Decision Tree Regressor

17Regression with Decision Tree True or False

18Building a Decision Tree Regressor


20Generalization Practice Questions

21What Did We Just Learn?

About this course

This course covers the data science perspective on the introductory concepts in machine learning, with a focus on making predictions. It covers how to build different models such as K-NN, decision trees and linear classifiers as well as important concepts such as data splitting and fundamental rules and laws. In addition, this course will teach you how to evaluate models properly and question their validity all while streamlining the process with pipelines.

About the program

The University of British Columbia (UBC) is a comprehensive research-intensive university, consistently ranked among the 40 best universities in the world. The Key Capabilities in Data Science program was launched in September 2020 and is developed and taught by many of the same instructors as the UBC Master of Data Science program.