Machine Learning Fundamentals Model Generalizability
About
An introduction to creating generalizable models using methods such as train, test, split, and describing the bias variance tradeoff.
Learning Objectives
You will be able to:
- Describe the principles of generalizability.
- Compare Bias and Variance and describe the tradeoff between when selecting a model.
- Define and apply the Train Test Split method using
scikit-learn. - Explore and apply the k-Folds Cross Validation method in
scikit-learn.
Content
| Topic | Skills |
|---|---|
| Slides | - Model Generalizability |
| Train Test Split and Cross-Validation | - Define and apply Train Test Split method in scikit-learn (TTS section of notebook)-- Explore and apply K-Folds Cross Validation method in scikit-learn (k-Folds section of notebook) |
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 :)