The preparations

You will find the code for this example here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/blob/master/Chapter05/FuelEfficiency.ipynb.

Loading the libraries

To follow this example, you need to install the following libraries:

• mldatasets to load the dataset
• pandas and numpy to manipulate it
• sklearn (scikit-learn), tensorflow, xgboost, and rulefit to split the data and fit the models
• scipy to perform statistical testing
• matplotlib, seaborn, shap, and alepython to visualize the interpretations

You should load all of them first:

import math
import os
import mldatasets
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import metrics, tree
import as tf
import tensorflow_docs as tfdocs
import tensorflow_docs.plots
import xgboost as xgb
from rulefit import RuleFit
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
import...