Implementing random forest

Let’s try to improve our heart disease model with a random forest:

1. First, let’s load the same libraries that we used in the previous section, except we will import the random forest classifier this time:

   import pandas as pd
   import numpy as np
   from imblearn.pipeline import make_pipeline
   from sklearn.ensemble import RandomForestClassifier
   from sklearn.model_selection import RandomizedSearchCV
   from scipy.stats import randint
   import sklearn.metrics as skmet
   import os
   import sys
   sys.path.append(os.getcwd() + "/helperfunctions")
   import healthinfo as hi

We also load the healthinfo module; it loads the health information data and does our preprocessing. There is nothing fancy here. The preprocessing code we stepped through earlier was just copied to the helperfunctions subfolder of the current working directory.

2. Now, let’s grab the data that’s been processed by the healthinfo module so that we can use it...