08_ml_regression

Machine learning: regression

By Gianluca Campanella (g.campanella@estimand.com)

Objectives

By the end of the session, you should be able to:

• Describe the link between prediction and loss functions
• Define the bias-variance trade-off
• Use scikit-learn to fit cross-validated, regularised linear models

Plan

The session is designed to be delivered over three hours (including breaks).

Topic	Time
Introduction to prediction	45 minutes
Linear regression using scikit-learn	45 minutes
Exercises	60 minutes

Materials

• Introduction to prediction
• Linear regression using scikit-learn
• Exercises and solutions

Additional resources

• Understanding the Bias-Variance Tradeoff
• scikit-learn documentation