Preparations

You will find most of the code for this example here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/blob/master/Chapter08/FruitClassifier_part1.ipynb, up to the Exploring Classifications with Pertubation-Based Attribution Methods section. That section alone is located here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/blob/master/Chapter08/FruitClassifier_part2.ipynb.

Loading the libraries

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

• mldatasets to load the dataset
• pandas, numpy, and sklearn (Scikit-learn) to manipulate it
• tensorflow to fit and predict with the models
• matplotlib, seaborn, cv2, skimage, tf-explain, tf-keras-vis, lime, alibi, and shap 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 import preprocessing, metrics
import tensorflow...