Using exhaustive feature selection

If your results from forward and backward selection are unpersuasive, and you do not mind running a model while you go out for coffee or lunch, you can try exhaustive feature selection. Exhaustive feature selection trains a given model on all possible combinations of features and selects the best subset of features. But it does this at a price. As the name suggests, this procedure might exhaust both system resources and your patience.

Let's use exhaustive feature selection for our model of bachelor's degree completion:

1. We start by loading the required libraries, including the RandomForestClassifier and LogisticRegression modules from scikit-learn and ExhaustiveFeatureSelector from mlxtend. We also import the accuracy_score module so that we can evaluate a model with the selected features:

   import pandas as pd
   from feature_engine.encoding import OneHotEncoder
   from sklearn.model_selection import train_test_split
   from sklearn.preprocessing...