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)
1
Section 1: Introduction to Machine Learning Interpretation
5
Section 2: Mastering Interpretation Methods
12
Section 3:Tuning for Interpretability

Leveraging SHAP's KernelExplainer for local interpretations with SHAP values

For this section, and for subsequent use, we will train a Support Vector Classifier (SVC) model first.

Training a C-SVC model

SVM is a family of model classes that operate in high-dimensional space to find an optimal hyperplane, where they attempt to separate the classes with the maximum margin between them. Support vectors are the points closest to the decision boundary (the dividing hyperplane) that would change it if were removed. To find the best hyperplane, they use a cost function called hinge loss and a computationally cheap method to operate in high-dimensional space, called the kernel trick, and even though a hyperplane suggests linear separability, it's not always limited to a linear kernel.

The scikit-learn implementation we will use is called C-SVC. SVC uses an L2 regularization parameter called C and, by default, uses a kernel called the radial basis function (RBF), which is...