The preparations

The code for this example can be found at https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python-2E/blob/main/10/Mailer.ipynb.

Loading the libraries

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

• mldatasets to load the dataset
• pandas, numpy, and scipy to manipulate it
• mlxtend, sklearn-genetic-opt, xgboost, and sklearn (scikit-learn) to fit the models
• matplotlib and seaborn to create and visualize the interpretations

To load the libraries, use the following code block:

import math
import os
import mldatasets
import pandas as pd
import numpy as np
import timeit
from tqdm.notebook import tqdm
from sklearn.feature_selection import VarianceThreshold,\
                                    mutual_info_classif, SelectKBest
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression,\
                                    LassoCV, LassoLarsCV...