This repository provides code examples for the Packt book "Interpretable Machine Learning with Python." It targets data scientists, ML developers, and data stewards seeking to understand and explain AI system behavior, mitigate bias, and build fairer models. The benefit is practical guidance on implementing various interpretability techniques.

How It Works

The repository is organized by book chapters, each containing Python code demonstrating specific interpretability methods. It covers intrinsically interpretable models (linear models, decision trees, Naïve Bayes) and model-agnostic techniques. The code utilizes libraries like scikit-learn, TensorFlow, SHAP, LIME, and others to visualize model workings and understand feature influences.

Quick Start & Requirements

• Installation: Recommended to install Anaconda for Python 3.6+ and required packages. Alternatively, use pip install -r requirements.txt or pip install --no-deps -r requirements.txt to manage potential conflicts.
• Prerequisites: Python 3.6+, matplotlib 3.2.2+, scikit-learn 0.22.2+, pandas 1.1.5+, numpy 1.19.5+, TensorFlow 2.4.1+, and various other specialized libraries depending on the chapter.
• Environment: Code can be run locally on Windows, macOS, or Linux, or in cloud environments like Google Colab. Compute-intensive notebooks may require a "High-RAM" runtime in Colab or a more powerful local setup (4 cores, 8GB RAM recommended).
• Resources: Links to specific Jupyter notebooks for each chapter are provided.

Highlighted Details

• Covers a wide range of interpretability techniques, from intrinsic model interpretability to model-agnostic methods.
• Includes practical examples for visualizing image classifier behavior and understanding learned features.
• Demonstrates methods for mitigating bias in datasets.
• Provides specific library version requirements for reproducibility.

Maintenance & Community

This repository is associated with a published book by Packt. No specific community channels (Discord, Slack) or active maintenance signals are mentioned in the README.

Licensing & Compatibility

The repository itself does not explicitly state a license. The code examples are intended for use with the accompanying book. Compatibility is primarily with Python 3.6+ and the specified library versions.

Limitations & Caveats

Some library installations may encounter conflicts, requiring specific installation flags. Notebooks marked with a '+' are compute-intensive and may run very slowly or fail on standard cloud environments like Google Colab without significant resource allocation.