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

Employing LIME

Until now, the model-agnostic interpretation methods we've covered attempt to reconcile the totality of outputs of a model with its inputs. For these methods to get a good idea of how and why X becomes y_pred, they need some data first. Then, they perform simulations with this data, pushing variations of it in and evaluating what comes out of the model. Sometimes, they even leverage a global surrogate to connect the dots. By using what they learned in this process, they yield importances, scores, rules, or values that quantify a feature's impact, interactions, or decisions on a global level. For many methods such as SHAP, these can be observed locally too. However, even when it can be observed locally, what was quantified globally may not apply locally. For this reason, there should be another approach that quantifies the local effects of features solely for local interpretation—one such as LIME!

What is LIME?

LIME trains local surrogates to explain...