Training classical machine learning models

We’ll start by training a model that lets us look inside it. We’ll use the CART decision tree classifier, where we can visualize the actual decision tree that is trained. We’ll use the same numerical data we used in the previous chapter. First, let’s read the data and create the train/test split:

# read the file with data using openpyxl
import pandas as pd
# we read the data from the excel file,
# which is the defect data from the ant 1.3 system
dfDataAnt13 = pd.read_excel('./chapter_6_dataset_numerical.xlsx',
                            sheet_name='ant_1_3',
                            index_col=0)
# prepare the dataset...