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

The preparations

You will find the code for this example here:

Loading the libraries

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

  • mldatasets to load the dataset
  • pandas, numpy, and nltk to manipulate it
  • sklearn (scikit-learn) and lightgbm to split the data and fit the models
  • matplotlib, seaborn, shap, and lime to visualize the interpretations

You should load all of them first, as follows:

import math
import mldatasets
import pandas as pd
import numpy as np
import re
import nltk
from nltk.probability import FreqDist
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn import metrics, svm
from sklearn.feature_extraction.text import TfidfVectorizer
import lightgbm as lgb
import matplotlib.pyplot as plt
import seaborn as sns
import shap
import lime