Feature selection
While preparing your data for modeling, it is important to ensure that you have a set of helpful features for the model to base its predictions (or diagnosis) on. In order to be helpful, features need to capture essential characteristics of the heart disease dataset in an orthogonal way; more data isn't always better!
You can use the sklearn.feature_selection.SelectFromModel module to select useful features. SelectFromModel implements a brute-force method that uses a RandomForestClassifier model to find the most salient features for the task of heart disease diagnosis.
RandomForestClassifier has been imported and the heart disease data features and target have been imported as X_train and y_train, respectively.
This exercise is part of the course
End-to-End Machine Learning
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
from sklearn.feature_selection import SelectFromModel
# Define the random forest model and fit to the training data
rf = ____(____=____, ____=____, ____=____)
rf.____(____, ____)