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Interpretable Machine Learning with Python

You're reading from Interpretable Machine Learning with Python Learn to build interpretable high-performance models with hands-on real-world examples

Product type Paperback

Published in Mar 2021

Publisher Packt

ISBN-13 9781800203907

Length 736 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

Serg Masís

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: Introduction to Machine Learning Interpretation

2. Chapter 1: Interpretation, Interpretability, and Explainability; and Why Does It All Matter? FREE CHAPTER

3. Chapter 2: Key Concepts of Interpretability

4. Chapter 3: Interpretation Challenges

5. Section 2: Mastering Interpretation Methods

6. Chapter 4: Fundamentals of Feature Importance and Impact

7. Chapter 5: Global Model-Agnostic Interpretation Methods

8. Chapter 6: Local Model-Agnostic Interpretation Methods

9. Chapter 7: Anchor and Counterfactual Explanations

10. Chapter 8: Visualizing Convolutional Neural Networks

11. Chapter 9: Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis

12. Section 3:Tuning for Interpretability

13. Chapter 10: Feature Selection and Engineering for Interpretability

14. Chapter 11: Bias Mitigation and Causal Inference Methods

15. Chapter 12: Monotonic Constraints and Model Tuning for Interpretability

16. Chapter 13: Adversarial Robustness

17. Chapter 14: What's Next for Machine Learning Interpretability?

18. Other Books You May Enjoy

The approach

Logistic regression is one common way to rank risk factors in medical use cases. Unlike linear regression, it doesn't try to predict a continuous value for each of your observations, but it predicts a probability score that an observation belongs to a particular class. In this case, what we are trying to predict is, given data for each patient, what is the probability, from 0 to 1, that they have cardiovascular disease?

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Authors (1)

Serg Masís

Serg Masís

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.

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