print "Performing greedy feature selection..."
score_hist = []
N = 10
good_features = set([])
# Greedy feature selection loop
while len(score_hist) < 2 or score_hist[-1][0] > score_hist[-2][0]:
scores = []
for f in range(len(Xts)):
if f not in good_features:
feats = list(good_features) + [f]
Xt = sparse.hstack([Xts[j] for j in feats]).tocsr()
score = cv_loop(Xt, y, model, N)
scores.append((score, f))
print "Feature: %i Mean AUC: %f" % (f, score)
good_features.add(sorted(scores)[-1][1])
score_hist.append(sorted(scores)[-1])
print "Current features: %s" % sorted(list(good_features))

注意还没结束:

# Remove last added feature from good_features
good_features.remove(score_hist[-1][1])

from kaggle

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