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/tree/main/04/UsedCars.ipynb

Loading the libraries

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

mldatasets to load the dataset
pandas and numpy to manipulate it
sklearn (scikit-learn) and catboost to load and configure the model
matplotlib, seaborn, shap, pdpbox, and pyale to generate and visualize the model interpretations

You should load all of them first:

import math
import os, random
import numpy as np
import pandas as pd
import mldatasets
from sklearn import metrics, ensemble, tree, inspection,\
                    model_selection
import catboost as cb
import matplotlib.pyplot as plt
import seaborn as sns
import shap
from pdpbox import pdp, info_plots
from PyALE import ale
from lime.lime_tabular import LimeTabularExplainer