SKlearn (scikit-learn) multivariate feature selection for regression
<p>I want to use a feature selection method where "combinations" of features or "between features" interactions are considered for a simple linear regression. </p>

<p>SelectKBest only looks at one feature to the target, one at a time, and ranks them by Pearson's R values. While this is quick, but I'm afraid it's ignoring some important interactions between features.</p>

<p>Recursive Feature Elimination first uses ALL my features, fits a Linear Regression model, and then kicks out the feature with the smallest absolute value coefficient. I'm not sure whether if this accounts for "between features" interaction...I don't think so since it's simply kicking out the smallest coefficient one at a time until it reaches your designated number of features.</p>

<p>What I'm looking for is, for those seasoned feature selection scientists out there, a method to find the best subset or combination of features. I read through all Feature Selection documentation and can't find a method that describes what I have in mind.</p>

<p>Any tips will be greatly appreciated!!!!!!</p>