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

The preparations

You will find most of the code for this example at https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/tree/master/Chapter13/Masks_part1.ipynb, up to the code used in the Certifying robustness with randomized smoothing section. The code for that section alone is located at https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/tree/master/Chapter13/Masks_part2.ipynb.

Loading the libraries

To run this example, you need to install the following libraries:

  • mldatasets to load the dataset
  • numpy and sklearn (scikit-learn) to manipulate it
  • tensorflow to fit the models
  • matplotlib and seaborn to visualize the interpretations

You should load all of them first, as follows:

import math
import os
import warnings
warnings.filterwarnings("ignore")
import mldatasets
import numpy as np
from sklearn import preprocessing
import tensorflow as tf
from tensorflow.keras.utils import get_file
import...