Book Image

Interpretable Machine Learning with Python - Second Edition

By : Serg Masís
4 (4)
Book Image

Interpretable Machine Learning with Python - Second Edition

4 (4)
By: Serg Masís

Overview of this book

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.
Table of Contents (17 chapters)
15
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16
Index

What is feature importance?

Feature importance refers to the extent to which each feature contributes to the final output of a model. For linear models, it’s easier to determine the importance since coefficients clearly indicate the contributions of each feature. However, this isn’t always the case for non-linear models.

To simplify the concept, let’s compare model classes to various team sports. In some sports, it’s easy to identify the players who have the greatest impact on the outcome, while in others, it isn’t. Let’s consider two sports as examples:

  • Relay race: In this sport, each runner covers equal distances, and the race’s outcome largely depends on the speed at which they complete their part. Thus, it’s easy to separate and quantify each racer’s contributions. A relay race is similar to a linear model since the race’s outcome is a linear combination of independent components.
  • Basketball...