Machine Learning Fundamentals Regression Metrics
About
An introduction to Regression Metrics: what they are, how they work, and how use them to assess regression models in scikit-learn.
Learning Objectives
You will be able to:
- Describe the principles of regression metrics.
- Define common metrics including error measures (MAE, MSE, RMSE), R2, Adj R2 and others.
- Apply regression metrics to assess OLS models.
Content
| Topic | About |
|---|---|
| Slides | - Describe the principles of regression metrics - Define common metrics including error measures (MAE, MSE, RMSE), R2, Adj R2 and others |
| 02 Regression Metrics | - Apply regression metrics to assess OLS models |
Prerequisites
- Write and run Python code in a Jupyter notebook.
- Create basic Python functions.
- Import and use
pandasandnumpy. - Create data visualizations in a noteboook using Matplotlib and Seaborn
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 :)