Machine Learning Fundamentals Regularization
About
Understand what regularization is, the different types you can apply (LASSO, Ridget and ElasticNet), and their impact on the bias-variance tradeoff. Also implement these types of regularized regression.
Learning Objectives
You will be able to:
- Define and describe regularization for regression models.
- Understand the causes of overfitting in linear models.
- Describe how regularization effects regression coefficients.
- Compare Ridge, Lasso, and ElasticNet regularization techniques.
- Implement regularized models using
scikit-learn.
Content
| Topic | About |
|---|---|
| Slides | - Overview of regularization |
| Regularization | - Compare Ridge, Lasso, and ElasticNet regularization techniques - Implement regularized models using scikit-learn |
Prerequisites
- Interpret and use Python dictionaries.
- Build
pandasDataFramesfrom dictionaries. - Perform simple data manipulation on
pandasobjects. - Understand bias / variance and implement train-test split.
- Machine learning workflow with
scikit-learn. - Understand the bias-variance tradeoff.
References
📖 Reference Materials
Important note
Please ignore the _layouts folder and the config.yml file in the root of this repo. They are needed to make sure the repo renders well; please don’t change them or delete them and your repo will work fine :)