title: SFS with unfixed k_features for regression, which scoring includes penalty of dimension?
body: <p>When use step function in R, the scoring criteria is AIC, so the penalty of dimension is part of the scoring. But when I use python, I don't know which scoring I should choose, especially when 'k_features' is not fixed.</p>
  <p>When I use 'k_features' = (1,8), the result of sfs.k_feature_idx_ is always just (0,)</p>
  <p><img src="https://i.stack.imgur.com/0fe6K.png" alt="enter image description here" /></p>
  <p>Can anyone tell me how to fix this?</p>