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results.txt
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results.txt
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features:
uncommon words?,
quotes?
one-letter variations,
named entity,
any word twice?
ss50 0.899289015735
{'logr__C': 0.015625, 'vect__char_range': (1, 4), 'vect__word_range': (1, 2)}
0.896956114795
{'logr__C': 0.01, 'vect__char_range': (1, 5), 'vect__word_range': (1, 3)}
ss5 0.904839161565
{'logr__C': 0.015625, 'vect__char_range': (1, 5), 'vect__word_range': (1, 3)}
min_df=2
ss50 0.897950956364 +-0.01042787
{'logr__C': 0.01, 'vect__char_range': (1, 5), 'vect__word_range': (1, 3)}
min_df=1
ss50 0.899167675844 +-0.00996715
{'logr__C': 0.015625, 'vect__char_range': (1, 5), 'vect__word_range': (1, 2)}
min_df=1 nochar
0.889458659739
{'logr__C': 1.0, 'vect__word_range': (1, 2)}
min_df=2 nochar
0.889937560119
{'logr__C': 0.5, 'vect__word_range': (1, 3)}
only designed ss20:
0.754171776832
{'logr__C': 0.125}
just words ss20, mindf2
0.881163938504
{'logr__C': 0.25, 'vect__word_range': (1, 3)}
no words: ss10
0.906459334679
{'logr__C': 4.0, 'vect__char_range': (1, 4)}
no words: ss20
0.906488000861
{'logr__C': 4, 'vect__char_range': (1, 4)}
no words, more params, ss10
0.907882622299
{'logr__C': 6, 'vect__char_range': (1, 5)}
no words, more params, ss20
0.904469490972
{'logr__C': 9, 'vect__char_range': (1, 5)}
no words, classif selection
0.909000343987
{'logr__C': 9, 'select__percentile': 30, 'vect__word_range': (1, 3), 'vect__char_range': (1, 5)}
no words, chi2 selection
0.909025637541
{'logr__C': 16.0, 'select__percentile': 30, 'vect__word_range': (1, 3), 'vect__char_range': (1, 5)}
no words, chi2 selection
0.910659667085
{'logr__C': 13, 'select__percentile': 10, 'vect__word_range': (1, 3), 'vect__char_range': (1, 5)}
0.910878313902
{'logr__C': 20, 'select__percentile': 7, 'vect__word_range': (1, 3), 'vect__char_range': (1, 5)}
precompute features, ss50: coarse selction :-/ throw away
0.907881815965
{'logr__C': 16.0, 'select__percentile': 6}
0.911123747335
{'logr__C': 15, 'select__percentile': 8}
0.911813319384
{'logr__C': 15, 'select__percentile': 11}
ss 50:
0.910450058337
{'logr__C': 12, 'select__percentile': 9}
feature selection on words + chars, chi2:
0.912640914406
{'logr__C': 4.0, 'select__percentile': 16, 'vect__word_range': (1, 3), 'vect__char_range': (1, 5)}
0.913432113519
{'logr__C': 8, 'select__percentile': 15}
with updated badwords:
0.915528921865
{'logr__C': 14, 'select__percentile': 15
with counting bad words:
0.91333840187
{'logr__C': 9, 'select__percentile': 13}
ss20 binary=False, words + char
0.911491117421
{'logr__C': 6, 'select__percentile': 7}
0.913358142081
{'logr__C': 4.0, 'select__percentile': 15}
0.914403292468
{'logr__C': 5, 'select__percentile': 8}
ss 20, tfidf, binary=False, no scaling of designed features
0.912261756081
{'logr__C': 7, 'select__percentile': 2}
with scaling of features:
0.912951813566
{'logr__C': 3, 'select__percentile': 10}
l1 select: ss5
char+words+designed, (1, 5), (1, 3)
0.897332385116
{'logr__C': 4.0, 'select__C': 8.0}
no select ss50:
0.903399470341
{'logr__C': 5}
min_df ss10
0.917669556837
{'logr__C': 2, 'select__percentile': 14, 'vect__words__min_df': 3}
ss10 new feature extraction. only words
0.893394841981
{'C': 4}
ne features, only words and stuff
0.903229621733
{'select__percentile': 16}
elasticnet: no select
0.906497374319
{'logr__alpha': 0.0001220703125, 'logr__rho': 0.8, 'logr__n_iter': 5}
0.912700394265
{'logr__alpha': 0.0001, 'logr__rho': 1, 'logr__n_iter': 11}
0.912545571475
{'logr__alpha': 0.0001, 'logr__rho': 0.94999999999999996, 'logr__n_iter': 21}
select percentile
0.914085740028
{'logr__alpha': 6.103515625e-05, 'select__percentile': 11}