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), rulefit, statsmodels, interpret, tf, and gaminet to fit models and calculate performance metrics
• matplotlib to create visualizations

Load these libraries as seen in the following snippet:

import math
import mldatasets
import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import PolynomialFeatures, StandardScaler,\
                                  MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn import metrics, linear_model, tree, naive_bayes,\
                    neighbors, ensemble, neural_network, svm
from rulefit import RuleFit
import statsmodels.api as sm
from interpret.glassbox import ExplainableBoostingClassifier
from interpret import show
from interpret.perf import ROC
import...