Machine Learning Fundamentals Model Explainability
About
An introduction to the importance of model explainability and interpretability. Learners will understand why this is important, which models tend to be more explainable, and how to assess a model’s explainability.
This is backed up with practical exercises and case studies.
Learning Objectives
You will be able to:
- Understand what model interpretability is, and why it’s important.
- Discuss real world examples of more and less explainable models.
- Understand and implement metrics including feature importance and decision tree visualisations as examples of explainability.
Content
| Topic | About |
|---|---|
| Slides | - Understand the intuition behind the k-NN algorithm |
| Model Explainability | - Prepare data for k-NN algorithm using standardization techniques - Implement KNN with scikit-learn |
Prerequisites
- Write and run Python code in a Jupyter notebook.
- Create basic Python functions.
- Have familiarity with
pandas. - Have familiarity with
scikit-learn.
References
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 :)