Chapter 02.01: Linear Models with L2 Loss
In this section, we focus on the general concept of linear regression and explain how the linear regression model can be used from a machine learning perspective to predict a continuous numerical target variable. Furthermore, we introduce the \(L2\) loss in the context of linear regression and explain how its use results in an SSE-minimal model.
Lecture video
Lecture slides
Quiz
---
shuffle_questions: false
---
## Which statements are true?
- [x] The target in linear regression has to be numeric.
- [ ] The features in linear regression have to be numeric.
- [x] The classical linear model from statistics with Gaussian errors is linear regression with $L2$ loss.
- [x] The hypothesis space of linear regression consists of linear functions of the features.