Exploring random forests

To get a better sense of how random forests work, let's build one using scikit-learn.

Random forest classifiers

Let's use a random forest classifier to predict whether a user makes more or less than USD 50,000 using the census dataset we cleaned and scored in Chapter 1, Machine Learning Landscape, and revisited in Chapter 2, Decision Trees in Depth. We are going to use cross_val_score to ensure that our test results generalize well:

The following steps build and score a random forest classifier using the census dataset:

1. Import pandas, numpy, RandomForestClassifier, and cross_val_score before silencing warnings:

   import pandas as pd
   import numpy as np
   from sklearn.ensemble import RandomForestClassifier
   from sklearn.model_selection import cross_val_score
   import warnings
   warnings.filterwarnings('ignore')

2. Load the dataset census_cleaned.csv and split it into X (a predictor column) and y (a target column):

   df_census = pd.read_csv...