 Название: An Introduction to Machine Learning Interpretability
Автор: Navdeep Gill, Patrick Hall
Издательство: O'Reilly Media
ISBN: 9781492033158
Год: 2018
Страниц: 45
Язык: английский
Формат: pdf (conv), djvu
Размер: 10.1 MB
Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate but also makes their predictions difficult to understand. When accuracy outpaces interpretability, human trust suffers, affecting business adoption, regulatory oversight, and model documentation.
Banking, insurance, and healthcare in particular require predictive models that are interpretable. In this ebook, Patrick Hall and Navdeep Gill from H2O.ai thoroughly introduce the idea of machine learning interpretability and examine a set of machine learning techniques, algorithms, and models to help data scientists improve the accuracy of their predictive models while maintaining interpretability.
Learn how machine learning and predictive modeling are applied in practice
Understand social and commercial motivations for machine learning interpretability, fairness, accountability, and transparency
Explore the differences between linear models and more accurate machine learning models
Get a definition of interpretability and learn about the groups leading interpretability research
Examine a taxonomy for classifying and describing interpretable machine learning approaches
Learn several practical techniques for data visualization, training interpretable machine learning models, and generating explanations for complex model predictions
Explore automated approaches for testing model interpretability
Table of Contents:
An Introduction to Machine Learning Interpretability
Machine Learning and Predictive Modeling in Practice
Social and Commercial Motivations for Machine Learning Interpretability
The Multiplicity of Good Models and Model Locality
Accurate Models with Approximate Explanations
Defining Interpretability
A Machine Learning Interpretability Taxonomy for Applied Practitioners
A Scale for Interpretability
Global and Local Interpretability
Model-Agnostic and Model-Specific Interpretability
Understanding and Trust
Common Interpretability Techniques
Seeing and Understanding Your Data
Techniques for Creating White-Box Models
Techniques for Enhancing Interpretability in Complex Machine Learning Models
Sensitivity Analysis: Testing Models for Stability and Trustworthiness
Testing Interpretability
Machine Learning Interpretability in Action
Conclusion
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An Introduction to Machine Learning Interpretability 
