Book description
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
- Common Interpretability Techniques
- Testing Interpretability
- Machine Learning Interpretability in Action
- Conclusion
Product information
- Title: An Introduction to Machine Learning Interpretability
- Author(s):
- Release date: April 2018
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492033141
You might also like
video
Machine Learning with Regression in Python: With Ordinary Least Squares, Ridge, Decision Trees and Neural Networks
In this video, you will learn regression techniques in Python using ordinary least squares, ridge, lasso, …
book
Python Machine Learning Cookbook - Second Edition
Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, …
book
Graph-Powered Machine Learning
Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. …
book
Practicing Trustworthy Machine Learning
With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations …