You're reading from Interpretable Machine Learning with Python Build explainable, fair, and robust high-performance models with hands-on, real-world examples

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781803235424

Length 606 pages

Edition 2nd Edition

Languages

Python

Tools

BERT

Concepts

Machine Learning

Author (1):

Serg Masís

View More author details

Table of Contents (17) Chapters

Preface

1. Interpretation, Interpretability, and Explainability; and Why Does It All Matter?

2. Key Concepts of Interpretability FREE CHAPTER

3. Interpretation Challenges

4. Global Model-Agnostic Interpretation Methods

5. Local Model-Agnostic Interpretation Methods

6. Anchors and Counterfactual Explanations

7. Visualizing Convolutional Neural Networks

8. Interpreting NLP Transformers

9. Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis

10. Feature Selection and Engineering for Interpretability

11. Bias Mitigation and Causal Inference Methods

12. Monotonic Constraints and Model Tuning for Interpretability

13. Adversarial Robustness

14. What’s Next for Machine Learning Interpretability?

15. Other Books You May Enjoy

16. Index

The preparations

You will find the code for this example here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python-2E/blob/main/05/ChocoRatings.ipynb

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 nltk.tokenize import word_tokenize
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...