# mingchaozhu/interpretablemlbook

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4,898 stars · 683 forks · GPL-3.0

## Links

- GitHub: https://github.com/MingchaoZhu/InterpretableMLBook
- awesome-repositories: https://awesome-repositories.com/repository/mingchaozhu-interpretablemlbook.md

## Description

InterpretableMLBook is a comprehensive Chinese translation of the guide to understanding and explaining black-box machine learning models. It serves as a technical reference and manual for applying model-agnostic techniques to interpret the internal logic of complex algorithms.

The resource focuses on black-box model analysis, providing a systematic approach to explaining individual predictions using methods such as Shapley values and LIME. It covers the evaluation of different interpretation methods to determine the most appropriate technique for a given project.

The content is organized into a modular chapter structure and generated from markdown files for electronic distribution.

## Tags

### Artificial Intelligence & ML

- [Black Box Model Interpretability](https://awesome-repositories.com/f/artificial-intelligence-ml/black-box-model-interpretability.md) — Breaking down the reasoning of opaque algorithms to ensure predictions are fair, accurate, and transparent.
- [Model Explainability](https://awesome-repositories.com/f/artificial-intelligence-ml/model-predictions/model-explainability.md) — Provides a systematic guide to interpreting the behavior of black-box machine learning algorithms using model-agnostic techniques. ([source](https://github.com/mingchaozhu/interpretablemlbook#readme))
- [Local Prediction Explanations](https://awesome-repositories.com/f/artificial-intelligence-ml/local-prediction-explanations.md) — Use specific techniques like Shapley values and LIME to explain the reasoning behind a single instance prediction. ([source](https://github.com/mingchaozhu/interpretablemlbook#readme))
- [Model Agnostic Explanations](https://awesome-repositories.com/f/artificial-intelligence-ml/model-agnostic-explanations.md) — Applying universal techniques to understand model behavior without relying on the specific underlying algorithm used.
- [Model Interpretability](https://awesome-repositories.com/f/artificial-intelligence-ml/model-interpretability.md) — Explains the decision logic and outputs of complex machine learning models for individual instances.
- [Interpretation Method Evaluations](https://awesome-repositories.com/f/artificial-intelligence-ml/interpretation-method-evaluations.md) — Analyze the strengths and weaknesses of different explanation techniques to select the most appropriate method for a project. ([source](https://github.com/mingchaozhu/interpretablemlbook#readme))
- [Machine Learning Concepts](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/machine-learning-concepts.md) — Teaches the fundamental principles of how to interpret the decision processes of complex ML models.

### Education & Learning Resources

- [Machine Learning Education](https://awesome-repositories.com/f/education-learning-resources/educational-resources/systems-applied-computing/machine-learning-education.md) — Provides educational materials for learning how to explain the internal logic of complex machine learning models. ([source](https://github.com/mingchaozhu/interpretablemlbook#readme))
- [ML Explainability Manuals](https://awesome-repositories.com/f/education-learning-resources/ml-explainability-manuals.md) — Serves as a systematic manual for applying methods like Shapley values and LIME to explain model predictions.
- [Translated Machine Learning Guides](https://awesome-repositories.com/f/education-learning-resources/translated-machine-learning-guides.md) — Provides a comprehensive Chinese translation of a technical guide on interpretable machine learning.

### Software Engineering & Architecture

- [Translation Mapping Systems](https://awesome-repositories.com/f/software-engineering-architecture/translation-mapping-systems.md) — Implements a mapping system to align Chinese translations with original English source text for conceptual accuracy.

### Part of an Awesome List

- [Learning and Reference](https://awesome-repositories.com/f/awesome-lists/ai/learning-and-reference.md) — Interpretable ML book (Chinese).