The preparations

You will find the code for this example here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/blob/master/Chapter03/FlightDelays.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), rulefit, cvae, statsmodels, interpret, and skope-rules to fit models and calculate performance metrics
• matplotlib and seaborn 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
from sklearn.model_selection import train_test_split
from sklearn import metrics, linear_model, tree, naive_bayes,\ 
  neighbors, ensemble, neural_network, svm, decomposition, manifold
from rulefit import...