logoIntroduction toMachine Learnin

Module 3: Splitting, Cross-Validation and the Fundamental Tradeoff

In this module, we will introduce why and how we split our data as well as how cross-validation works on training data. We will also explain two important concepts in machine learning: the fundamental tradeoff and the golden rule.

0Module Learning Outcomes

1Splitting Data

2Splitting our data

3Decision Tree Outcome

4Splitting Data in Action

5Train, Validation and Test Split

6Name that split!

7Decision Tree Outcome

8Cross Validation

9Cross Validation Questions

10Cross Validation True or False

11Cross Validation in Action

12Cross Validation in Action again!

13Underfitting and Overfitting

14Is it Overfitting or Underfitting?

15Overfitting and Underfitting True or False

16Overfitting/Underfitting in Action!

17Fundamental Tradeoff and the Golden Rule

18Quick Questions on Tradeoff and Golden Rule

19Training and Testing Questions

20Picking your Hyperparameter

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.