The preparations

You will find the code for this example here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/blob/master/Chapter07/Recidivism_part1.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), catboost, and tensorflow to split the data and fit the models
• matplotlib, seaborn, alibi, 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 tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import seaborn as sns
from alibi.utils.mapping import ohe_to_ord, ord_to_ohe
from alibi.explainers...