The preparations

You will find the code for this example here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python-2E/blob/main/06/Recidivism.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 the dataset
• sklearn (scikit-learn), and catboost to split the data and fit the models
• matplotlib, seaborn, alibi, tensorflow, shap, and witwidget to visualize the interpretations

You should load all of them first:

import math
import mldatasets
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import metrics
from catboost import CatBoostClassifier
import matplotlib.pyplot as plt
import seaborn as sns
from alibi.utils.mapping import ohe_to_ord, ord_to_ohe
import tensorflow as tf
from alibi.explainers import AnchorTabular, CounterFactualProto
import shap
import...