The preparations

You will find the code for this example here:

https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/tree/master/Chapter12/Recidivism_part2.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), xgboost, lightgbm, catboost, tensorflow, bayes_opt, and tensorflow_lattice to split the data and fit the models
• matplotlib, seaborn, scipy, xai, and shap to visualize the interpretations

You should load all of them first, as follows:

import math
import os
import copy
import mldatasets
import pandas as pd
import numpy as np
from sklearn import preprocessing, model_selection, metrics,\
     linear_model, svm, neural_network, ensemble
import xgboost as xgb
import lightgbm as lgb
import catboost as cb
import tensorflow as tf
from bayes_opt import BayesianOptimization...