The preparations

You will find the code for this example here:

https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/blob/master/Chapter06/ChocoRatings.ipynb

Loading the libraries

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

• mldatasets to load the dataset
• pandas, numpy, and nltk to manipulate it
• sklearn (scikit-learn) and lightgbm to split the data and fit the models
• matplotlib, seaborn, shap, and lime to visualize the interpretations

You should load all of them first, as follows:

import math
import mldatasets
import pandas as pd
import numpy as np
import re
import nltk
from nltk.probability import FreqDist
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn import metrics, svm
from sklearn.feature_extraction.text import TfidfVectorizer
import lightgbm as lgb
import matplotlib.pyplot as plt
import seaborn as sns
import shap
import lime
import...