Book Image

Interpretable Machine Learning with Python

By : Serg Masís
Book Image

Interpretable Machine Learning with Python

By: Serg Masís

Overview of this book

Do you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.
Table of Contents (19 chapters)
Section 1: Introduction to Machine Learning Interpretation
Section 2: Mastering Interpretation Methods
Section 3:Tuning for Interpretability

The approach

No single interpretation method is perfect, and even in the best scenario can only tell you one part of the story. Therefore, you have decided to first assess the model's predictive performance using traditional interpretation methods including the following:

  • ROC curves and ROC-AUC
  • Confusion matrices and all metrics derived from them (accuracy, precision, recall, F1).

Then, you'll examine the model using two activation-based methods:

  • Intermediate activation
  • Activation maximization

This is followed by evaluating decisions with three gradient-based methods:

  • Saliency maps
  • Grad-CAM
  • Integrated gradients

This is followed by three perturbation-based methods:

  • Occlusion sensitivity
  • LIME
  • CEM

And, lastly, a bonus backpropagation-based method:

  • SHAP's DeepExplainer

I hope that you understand why the model is not performing as it should and how to fix it by the end of this...