The preparations

You will find the code for this example here:

https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/blob/master/Chapter04/BirthOrder.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) to split the data and fit the models
• matplotlib and pdpbox to visualize the interpretations

You should load all of them first, using the following code:

import math
import mldatasets
import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler 
from sklearn.model_selection import train_test_split
from sklearn import metrics, linear_model, tree,\
        discriminant_analysis, ensemble, neural_network, inspection
import matplotlib.pyplot as plt
from pdpbox import pdp

Now, we can continue...