Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world exa

A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features:

• Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores
• Build your interpretability toolkit with global, local, model-agnostic, and model-specific methods
• Analyze and extract insights from complex models from CNNs to BERT to time series models

Book Description:

Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.

Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.

In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.

By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.

What You Will Learn:

• Progress from basic to advanced techniques, such as causal inference and quantifying uncertainty
• Build your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformers
• Use monotonic and interaction constraints to make fairer and safer models
• Understand how to mitigate the influence of bias in datasets
• Leverage sensitivity analysis factor prioritization and factor fixing for any model
• Discover how to make models more reliable with adversarial robustness

Who this book is for:

This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It's also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.

深入探討機器學習可解釋性的關鍵方面和挑戰，使用全面的工具包，包括SHAP、特徵重要性和因果推論，以建立更公平、更安全、更可靠的模型。

購買印刷版或Kindle電子書，將包含一本免費的PDF電子書。

主要特點：
- 解釋現實世界的數據，包括心血管疾病數據和COMPAS再犯分數
- 用全局、局部、模型無關和模型特定的方法建立您的可解釋性工具包
- 分析和提取從卷積神經網絡到BERT到時間序列模型的複雜模型的見解

書籍描述：
《Python可解釋機器學習第二版》揭示了通過分析現實世界數據來解釋機器學習模型的關鍵概念，為您提供了一系列技能和工具，以解讀甚至最複雜模型的結果。

通過多個使用案例來建立您的可解釋性工具包，從航班延誤預測到廢物分類到COMPAS風險評估分數。本書充滿了有用的技巧，將它們引入到正確的使用案例中。學習傳統方法，如特徵重要性和部分依賴圖，以及用於NLP解釋的集成梯度和基於梯度的歸因方法，如显著性地圖。

除了逐步的代碼，您還將通過減少複雜性、減輕偏見、設置防護措施和增強可靠性來實際操作調整模型和訓練數據以實現可解釋性。

通過閱讀本書，您將能夠自信地應對使用表格、語言、圖像和時間序列數據的黑盒模型的可解釋性挑戰。

學到的內容：
- 從基本到高級技術的進展，如因果推論和量化不確定性
- 從分析線性和邏輯模型到複雜模型，如CatBoost、CNN和NLP transformer，建立您的技能組
- 使用單調性和交互作用約束來建立更公平和更安全的模型
- 了解如何減輕數據集中偏見的影響
- 利用敏感性分析因素優先級和因素修復任何模型
- 發現如何通過對抗韌性使模型更可靠

本書適合數據科學家、機器學習開發人員、機器學習工程師、MLOps工程師和數據管理人員，他們對於解釋他們開發的人工智能系統的工作原理、對決策的影響以及如何識別和管理偏見具有越來越重要的責任。對於自學的機器學習愛好者和初學者來說，這也是一個有用的資源，但需要對Python編程語言有良好的理解以實施示例。