Exhaustively feature selection in scikit-learn? -


is there built in way of doing brute-force feature selection in scikit-learn ? i.e. exhaustively evaluate possible combinations of input features, , find best subset. familiar "recursive feature elimination" class interesting in evaluate possible combinations of input features 1 after other.

no, best subset selection not implemented. easiest way write yourself. should started:

from itertools import chain, combinations sklearn.cross_validation import cross_val_score  def best_subset_cv(estimator, x, y, cv=3):     n_features = x.shape[1]     subsets = chain.from_iterable(combinations(xrange(k), k + 1)                                   k in xrange(n_features))      best_score = -np.inf     best_subset = none     subset in subsets:         score = cross_val_score(estimator, x[:, subset], y, cv=cv).mean()         if score > best_score:             best_score, best_subset = score, subset      return best_subset, best_score

this performs k-fold cross-validation inside loop, fit k 2 ᵖ estimators when giving data p features.


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