Decision tree models

We will work with the heart disease data again in this chapter. This will be a great way to compare our results from the logistic regression model to those of a non-parametric model such as a decision tree. Follow these steps:

1. First, we load the same libraries that we have been using so far. The new modules are DecisionTreeClassifier from scikit-learn and SMOTENC from Imbalance Learn, which will help us deal with imbalanced data:

   import pandas as pd
   import numpy as np
   from sklearn.model_selection import train_test_split
   from sklearn.preprocessing import OneHotEncoder
   from imblearn.pipeline import make_pipeline
   from sklearn.compose import ColumnTransformer
   from sklearn.model_selection import RandomizedSearchCV
   from imblearn.over_sampling import SMOTENC
   from sklearn.tree import DecisionTreeClassifier, plot_tree
   from scipy.stats import randint
   import sklearn.metrics as skmet
   import os
   import sys
   sys.path.append(os.getcwd() + "/helperfunctions")
   from...