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Book Image

Interpretable Machine Learning with Python

Book Image

Interpretable Machine Learning with Python

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

Generating LSTM attributions with integrated gradients

We first learned about integrated gradients (IG) in Chapter 8, Visualizing Convolutional Neural Networks. Unlike the other gradient-based attribution methods studied in that chapter, path-integrated gradients is not contingent on convolutional layers, nor is it limited to classification problems. In fact, since it computes the gradients of the output concerning the inputs averaged along the path, the input and output could be anything! It is common to use integrated gradients with CNNs and Recurrent Neural Networks (RNNs), like the one we are interpreting in this chapter. Frankly, when you see an IG LSTM example online, it has an embedding layer and is an NLP classifier, but IG could be used very effectively for LSTMs that even process sounds or genetic data!

The integrated gradient explainer and the explainers that we will use moving forward can access any part of the traffic dataset. First, let's create a generator for...

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