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

Interpreting PDPs

A PDP conveys the marginal effect of a feature on the prediction throughout all (or interpolated) possible values for that feature. It's a global model interpretation method that can visually demonstrate the impact of a feature and the nature of the relationship with the target (linear, exponential, monotonic, and so on).

It can also be extended to include two features, to illustrate the effect of their interaction on the model. One feature plot shows in the y axis the predicted outcome or relative change in this outcome, and the x axis shows all possible values of the feature. The plotted line is calculated by changing the value of the feature to the one in the x axis for all the observations and averaging the predictions if this single feature were to change, to get the y axis coordinate.

One variation of the PDP deducts the expected value for all observations from the y axis, thus centering the marginal effect to the expected value. Another PDP variation...