https://github.com/solegalli/machine-learning-interpretability

Code repository for the online course Machine Learning Interpretability
https://github.com/solegalli/machine-learning-interpretability

explainable-ai explainable-machine-learning interpretable-machine-learning machine-learning machine-learning-algorithms

Last synced: 10 months ago
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Code repository for the online course Machine Learning Interpretability

• Host: GitHub
• URL: https://github.com/solegalli/machine-learning-interpretability
• Owner: solegalli
• License: other
• Created: 2023-06-08T09:26:11.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-10-12T17:49:22.000Z (over 1 year ago)
• Last Synced: 2025-03-29T03:22:44.062Z (11 months ago)
• Topics: explainable-ai, explainable-machine-learning, interpretable-machine-learning, machine-learning, machine-learning-algorithms
• Language: Jupyter Notebook
• Homepage: https://www.trainindata.com/courses/enrolled/2106490
• Size: 23.9 MB
• Stars: 25
• Watchers: 1
• Forks: 18
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

          
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## Machine Learning Interpretability- Code Repository

Code repository for the online course [Machine Learning Interpretability](https://www.trainindata.com/p/machine-learning-interpretability)

**Course launch: 30th November, 2023**

Actively maintained.

[](https://www.trainindata.com/p/machine-learning-interpretability)

## Table of Contents

1. **Machine Learning Interpretability**

	1. Interpretability in the context of Machine Learning

	2. Local vs Global Interpretability

	3. Intrinsically explainable models

	4. Post-hoc explainability methods

	5. Challenges to interpretability

	6. How to make models more explainable

2. **Intrinsically Explainable Models**

	1. Linear and Logistic Regression 

	2. Decision trees

	3. Random forests

	4. Gradient boosting machines

	5. Global and local interpretation

3. **Post-hoc methods - Global explainability**

	1. Permutation Feature Importance

	2. Partial dependency plots

	3. Accumulated local effects

4. **Post-hoc methods - Local explainability**

	1. LIME

	2. SHAP

	3. Individual contitional expectation

5. **Featuring the following Python interpretability libraries**

   	1. Scikit-learn

   	2. treeinterpreter

   	3. Eli5

   	4. Dalex

   	5. Alibi

   	6. pdpbox

   	7. Lime

   	8. Shap

## Links

- [Online Course](https://www.trainindata.com/p/machine-learning-interpretability)