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.
publication date
2023-10-31
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
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
approach
Complete with step-by-step explanations of essential concepts and practical examples, you will begin exploring the importance of interpretability in machine learning and applying methods to interpret models. You will also learn to implement modern techniques to build black-box models that overcome interpretability challenges.
audience
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.
short description
This hands-on book will help you make your machine learning models fairer, safer, and more reliable and in turn improve business outcomes. Every chapter introduces a new mission where you learn how to apply interpretation methods to realistic use cases with methods that work for any model type as well as methods specific for deep neural networks.
subtitle
Build explainable, fair, and robust high-performance models with hands-on, real-world examples
keywords
hands-on machine learning; machine learning python; ai/ml; natural language processing; transformers; explainable ai; xai; machine learning book; hands on machine learning; ai and machine learning; ml with python
Product ISBN
9781803235424
