The preparations

You will find the code for this example here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/blob/master/Chapter11/CreditCardDefaults.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, aif360, and lightgbm to split the data and fit the models
• matplotlib, seaborn, and xai to visualize the interpretations
• econml and dowhy for causal inference

You should load all of them first, as follows:

import math
import os
import mldatasets
import pandas as pd
import numpy as np
from tqdm.notebook import tqdm
from sklearn import model_selection, tree
import lightgbm as lgb
import xgboost as xgb
from aif360.datasets import BinaryLabelDataset
from aif360.metrics import BinaryLabelDatasetMetric,\
                           ClassificationMetric
from aif360.algorithms...