The preparations

You will find the code for this example here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python-2E/tree/main/04/UsedCars.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) and catboost to load and configure the model
• matplotlib, seaborn, shap, pdpbox, and pyale to generate and visualize the model interpretations

You should load all of them first:

import math
import os, random
import numpy as np
import pandas as pd
import mldatasets
from sklearn import metrics, ensemble, tree, inspection,\
                    model_selection
import catboost as cb
import matplotlib.pyplot as plt
import seaborn as sns
import shap
from pdpbox import pdp, info_plots
from PyALE import ale
from lime.lime_tabular import LimeTabularExplainer

The following snippet of code will load...