03 Regression
Preparation¶
Ch 4 (except "Logistic Regression")
Material¶
Session material: In this folder.
Regression - Hitters (Jupyter Notebook)
Session Description¶
Today, we are going to look at regression algorithms, where instead of predicting a class you predict some continuous variable.
We also discuss what is meant by “regularization” and consider the R² performance metric for regression.
Key Concepts¶
- Ordinary Least Squares (OLS) regression
- Ridge regression
- Lasso regression
Learning Objectives¶
- Explain what is meant by "regression" and in which contexts it applies
- Explain the following linear regression models, their strengths and weaknesses, and apply them in python:
- Ordinary Least Squares (OLS) regression
- Ridge regression
- Lasso regression
- Elastic Net Regression
- Explain what is meant by the term "regularization" in an ML-context
- Describe what is meant by "bias" and "variance" in relation to ML-algorithms
- Explain the R²-metric for evaluating the performance of a linear regression algorithm
- Explain the MSE metric
Video Lectures¶
3.1 Linear Regression¶
3.2 Regularization¶
In general we recommend Alexander Ihler’s course “Machine Learning and Data Mining”.