XAI-tutorial

A course on explainable AI with Python. You can access the course here.

File Structure and Descriptions

The resources in the src folder are as follows:

Folder	File	Dataset	Chapter	Description
intro	human_friendly_explanations.ipynb	insurance.csv	4	Using SHAP plots as the basis for human-friendly explanations
linear_models	power_of_linear_models.ipynb	performance.csv	5	Demonstrating how feature engineering allows linear models to model non-linear relationships
	feature_engineering.ipynb	credit_score.csv	6	Interpretable feature engineering methods
	feature_clustering.ipynb	credit_score.csv	8	Feature selection with hierarchical feature clustering
	linear_regression.ipynb	credit_score.csv	9	Explaining linear regression to a non-technical audience
	challenge_notebook.ipynb	credit_score.csv	5-9	Logistic regression model for Part 2 challenge
model_agnostic	feature_importance.ipynb	credit_score.csv	11	Calculating PFI scores from scratch
	pdp_and_ice.ipynb	PDP_ICE.csv	12	Applying PDPs and ICE plots with scikit-learn
	ale.ipynb	abalone.data	13	Applying ALEs with Alibi Explained
	h_stat.ipynb	abalone.data	14	Applying Friedman's H-stat with artemis
	lime.ipynb	abalone.data	15	Applying lime
	shap.ipynb	credit_score.csv	16	Applying SHAP
misc	transaction_data_generator.ipynb	-	-	Used to generate credit_score dataset

Datasets

The datasets used by the files can be found in the data folder. More information about the datasets is available at these links:

• credit_score.csv
• insurance.csv
• abalone.data
• PDP_ICE.csv
• performance.csv

Packages and Versions

For the majority of the course, we use:

• Python 3.11.5
• requirements.txt

For Chapter 13's notebook, ale.ipynb, we use:

• Python 3.9.12
• requirements_ale.txt