✓ ✓ R2: 58.45% Features: ['age' 'bmi' 'bp' 's2' 's5'] Bonus (+20 pts) Research SkLearn's method for selecting features. Use it and compare your selected features with it. Why might they be different features? RFE and/or Sequential FeatureSelector (10 pts each) [9] ['sex' 'bmi' 'bp' 's1' 's2' 's4' 's5']/nпо от TK UO Question We examined the diabetes data set in lecture using linear regression. We saw that too few features resulting in poor fit and too many features resulted in poor fit. Devise a method for for testing each combination of features from each as the only a single feature to all features together. Write code to try various combinations of features. Determine which feature combination is best to achieve the highest performance on the test data set. How many combinations need to be tested? # Load data import numpy as np. import pandas as pd. import sklearn.model_selection df = pd.read_csv("19.1.1 diabetes_data.csv", index_col=0) display(df)

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