Interpretable Machine Learning with Python
Packt Publishing Limited (Verlag)
978-1-80323-542-4 (ISBN)
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 DescriptionInterpretable 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 forThis 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.
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly. Causality Advocate, Bestselling Author, AI Researcher & Strategist Expert in AI Transformers including ChatGPT/GPT-4, Bestselling Author
Table of Contents
Interpretation, Interpretability and Explainability; and why does it all matter?
Key Concepts of Interpretability
Interpretation Challenges
Global Model-agnostic Interpretation Methods
Local Model-agnostic Interpretation Methods
Anchors and Counterfactual Explanations
Visualizing Convolutional Neural Networks
Interpreting NLP Transformers
Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
Feature Selection and Engineering for Interpretability
Bias Mitigation and Causal Inference Methods
Monotonic Constraints and Model Tuning for Interpretability
Adversarial Robustness
What's Next for Machine Learning Interpretability?
| Erscheinungsdatum | 27.09.2022 |
|---|---|
| Vorwort | Aleksander Molak, Denis Rothman |
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-10 | 1-80323-542-X / 180323542X |
| ISBN-13 | 978-1-80323-542-4 / 9781803235424 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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