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

Mitigating bias

We can mitigate bias at three different levels with methods that operate at these individual levels:

  • Preprocessing: These are interventions to detect and remove bias from the training data before training the model. Methods that leverage preprocessing have the advantage that they tackle bias at the source. On the other hand, any undetected bias is still amplified by the model.
  • In-processing: These methods mitigate bias during the model training and are, therefore, highly dependent on the model and tend to not be model-agnostic like the preprocessing and post-processing methods. They also require hyperparameter tuning to calibrate fairness metrics.
  • Post-processing: These methods mitigate bias during model inference. In Chapter 7, Anchors and Counterfactual Explanations, we touched on the subject of using the What-If tool to choose the right thresholds (see Figure 7.14 in that chapter), and we manually adjusted them to achieve parity with false positives...