"He Said She Said" classification challenge

Guess whether a text in Polish was written by a man or woman. [ver. 1.0.0]

# submitter when ver. description dev-0 Accuracy test-A Accuracy
85 [anonymised] 2019-02-18 18:06 1.0.0 My solution N/A N/A
84 [anonymised] 2017-02-27 17:02 1.0.0 moje rozwiazanie 1 N/A N/A
25 [anonymised] 2016-06-23 09:01 1.0.0 3gram model KenLM + stemming 0.6688909559051509 0.6564756690423372
5 [anonymised] 2016-06-19 20:27 1.0.0 New 3 best voting 0.7192677370216093 0.70309623825931
7 [anonymised] 2016-06-18 15:05 1.0.0 LSTM + ReLU + Softmax 0.6999905636214125 0.6932257988389107
6 [anonymised] 2016-06-18 09:25 1.0.0 RNN + more layers + ReLU 0.702632749625915 0.6965415113041016
26 [anonymised] 2016-06-17 21:37 1.0.0 KenLM + stemming + 2grams 0.6672126285706582 0.6561393779204229
4 [anonymised] 2016-06-17 15:47 1.0.0 RNN + more layers 0.7067982367452582 0.70309623825931
3 [anonymised] 2016-06-17 09:52 1.0.0 3 best voting 0.7142799369110688 0.7047009958937084
24 [anonymised] 2016-06-17 09:05 1.0.0 3gram model test-A KenLM + stemming 0.6672193688410779 0.6564756690423372
27 [anonymised] 2016-06-17 09:03 1.0.0 2gram model KenLM + stemming testA 0.6672193688410779 0.6560803794779818
8 [anonymised] 2016-06-17 09:00 1.0.0 RNN - LSTM - 2 layers - 3 epch 0.6929402407624594 0.6872964553735781
80 [anonymised] 2016-06-17 08:59 1.0.0 2gram model KenLM + stemming 0.6672193688410779 N/A
79 [anonymised] 2016-06-16 09:56 1.0.0 KenLM + stemming + 3grams 0.671674687588466 N/A
16 [anonymised] 2016-06-13 13:36 1.0.0 Both directions LSTM 0.680457259945269 0.6733846226459621
37 [anonymised] 2016-06-09 12:42 1.0.0 1M words + CONVOLUTION + LSTM 0.6549790377589949 0.6441095955066787
9 [anonymised] 2016-06-06 22:21 1.0.0 LSTM + 1M words 0.6945781264744342 0.6861518855902204
51 [anonymised] 2016-06-04 15:54 1.0.0 RNN - GRU & 5 epoch & 50k words 0.6198217872501045 0.6103860858073347
36 p/tlen 2016-05-30 20:51 1.0.0 simple NN trained on all (3 passes) with logistic regression 0.6506450438791604 0.6452659649785246
38 p/tlen 2016-05-30 19:40 1.0.0 simple NN trained on all (3 passes) 0.6471738046130411 0.6428057299287299
39 p/tlen 2016-05-30 10:41 1.0.0 simple NN train on all 0.6462503875655491 0.6408174824184641
41 p/tlen 2016-05-30 07:48 1.0.0 simple NN trained on 1M utterances 0.6410199377199013 0.6348409401991787
78 p/tlen 2016-05-30 07:46 1.0.0 simple NN with 1M utterances 0.6410199377199013 N/A
77 p/tlen 2016-05-29 20:09 1.0.0 skeleton for NN solutions 0.4042949003114005 0.4082692216925473
18 [anonymised] 2016-05-25 06:51 1.0.0 Doc2vec + 50k words + LR 0.6672598104635958 0.6597028838438665
62 [anonymised] 2016-05-24 19:09 1.0.0 Doc2vec + LR 0.6038540866259554 0.5844621701987067
40 [anonymised] 2016-05-24 17:13 1.0.0 lemma + nozdi naive bayes 0.6604386567989108 0.6398853070278945
29 [anonymised] 2016-05-24 11:55 1.0.0 klon rozwiazania Mateusza + RandomForestClassifier 0.6727598711260295 0.6529239628073819
17 [anonymised] 2016-05-23 19:11 1.0.0 Logistic Regression + Hashing Vectorizer - in memory 0.6800124020975722 0.6708181903997734
23 [anonymised] 2016-05-23 18:19 1.0.0 ANN + 3gram + hashing vectorizer 0.6602364486863213 0.6567057629678577
70 [anonymised] 2016-05-23 16:04 1.0.0 Another 100k sample with RandomForestClassifier 0.5532616168560682 0.5534997876056073
49 [anonymised] 2016-05-23 13:20 1.0.0 more iterations + randomized start 0.6214664132324988 0.6135189031009581
19 [anonymised] 2016-05-23 04:15 1.0.0 nozdi Naive Bayes + Tfidf + swear words + emoticons 0.6763996171526402 0.6583164204465002
21 [anonymised] 2016-05-22 13:15 1.0.0 nozdi Naive Bayes + Tfidf + swear words + emoticons v2 0.6763726560709615 0.658304620758012
68 [anonymised] 2016-05-18 20:07 1.0.0 100k sample TFIDF + RFC 0.5575821301950634 0.5588155472695521
69 [anonymised] 2016-05-18 18:21 1.0.0 100k sample CV + RFC 0.5593548213154311 0.5580898664275263
63 [anonymised] 2016-05-18 14:16 1.0.0 1mln sample with RandomForestClassifier 0.580188997182567 0.583683390758484
20 [anonymised] 2016-05-17 05:14 1.0.0 nozdi Naive Bayes + Tfidf + swear words + emoticons 0.6763726560709615 0.658304620758012
66 [anonymised] 2016-05-16 21:54 1.0.0 400k sample RandomTreeClassifier 0.5717569188875857 0.5713822155095105
67 [anonymised] 2016-05-16 19:44 1.0.0 200k sample size with RandomForestClassifier 0.5654008438818565 0.5650457827913343
47 [anonymised] 2016-05-16 16:08 1.0.0 100k samle with RandomForestClassifier 0.6359377738234858 0.6191768537310615
1 p/tlen 2016-05-15 18:31 1.0.0 VW tokens + 3-gram LM (+fix for the latest grep) 0.7231029508903897 0.7107778354651437
60 [anonymised] 2016-05-14 14:55 1.0.0 Added naive_bayes.py 0.6201453202302476 0.6006985415585029
59 [anonymised] 2016-05-14 14:44 1.0.0 char ngrams, sample train on 100k 0.6201453202302476 0.6006985415585029
50 [anonymised] 2016-05-09 16:36 1.0.0 Logistic regression (partial fit, iter_n=1, alpha=0.00005) 0.6213113870128469 0.6129112191438146
58 [anonymised] 2016-05-09 16:06 1.0.0 Logistic regression (partial fit, iter_n=5) 0.6084913926746741 0.6022030018407514
56 [anonymised] 2016-05-08 21:35 1.0.0 Logistic regression (partial fit) 0.6088081853843976 0.6023091990371454
57 [anonymised] 2016-05-08 21:28 1.0.0 Logistic regression (partial fit) 0.6088081853843976 0.6023091990371454
22 [anonymised] 2016-05-07 21:24 1.0.0 nozdi Naive Bayes + Tfidf 0.6763726560709615 0.658304620758012
31 [anonymised] 2016-05-04 19:04 1.0.0 Ensemble Multinomial NB+ BernoulliNB 0.6673676547903102 0.6473486099966961
30 [anonymised] 2016-04-23 12:29 1.0.0 Naive bayes 0.6727666113964492 0.6529239628073819
28 [anonymised] 2016-04-23 09:10 1.0.0 Naive bayes 0.6727666113964492 0.6529239628073819
44 [anonymised] 2016-04-23 08:43 1.0.0 Naive bayes 0.6727666113964492 0.6317848208807287
45 [anonymised] 2016-04-22 09:21 1.0.0 Naive bayes with stop words 0.6723824159825292 0.6293186859866899
35 [anonymised] 2016-04-20 06:55 1.0.0 Naive bayes 0.6640514417438428 0.6463220370982206
34 [anonymised] 2016-04-19 17:57 1.0.0 Naive bayes 0.6640514417438428 0.6463220370982206
61 p/tlen 2016-03-24 22:01 1.0.0 Vowpal Wabbit -nn 6 on morphosyntactic tags 0.5950243323762149 0.5917425779959409
55 p/tlen 2016-03-24 21:40 1.0.0 6-gram LM on morphosyntactic tags 0.598711260295763 0.6058255062066361
54 p/tlen 2016-03-24 21:21 1.0.0 6-gram LM on morphosyntactic tags 0.6895701055526348 0.6058255062066361
2 p/tlen 2016-02-20 12:48 1.0.0 VW tokens + 3-gram LM 0.7232040549466845 0.7106657384245056
13 p/tlen 2016-02-19 21:30 1.0.0 3-gram language model 0.6895701055526348 0.6798626516259971
10 p/tlen 2016-02-19 20:28 1.0.0 VW tokens + 300 V2W classes 0.6910866663970558 0.6850899136262802
11 p/tlen 2016-02-19 13:15 1.0.0 VW tokens + 5-suffixes + NN 0.6910057831520201 0.6842993344975693
14 p/tlen 2016-02-19 07:51 1.0.0 VW tokens + 5-prefixes + NN 0.6844070584111834 0.6792903667343182
12 p/tlen 2016-02-18 20:05 1.0.0 Vowpal Wabbit on tokens only + small NN 0.6883838179587765 0.6832727615990938
15 p/tlen 2016-02-18 19:53 1.0.0 Vowpal Wabbit on tokens only 0.6793518555964465 0.6754908670411102
43 [anonymised] 2016-02-15 18:27 1.0.0 Fixed source code, added makefile. 0.6402919885145792 0.6340503610704677
53 [anonymised] 2016-02-15 18:16 1.0.0 Fixed source code, added makefile.' 0.6137353230611612 0.6092238164912447
52 [anonymised] 2016-02-15 16:47 1.0.0 Logistic regression on words, punctuation n-grams, and suffixes. 0.6137353230611612 0.6092238164912447
42 [anonymised] 2016-02-13 23:09 1.0.0 Logistic regression on words and punctuation n-grams. N/A 0.6340503610704677
83 [anonymised] 2016-02-13 23:00 1.0.0 Logistic regression on words and punctuation n-grams. N/A N/A
48 [anonymised] 2016-02-12 09:24 1.0.0 Logistic regression, where features are unique, lowercased words longer than one character,truncated after the 5th if necessary. N/A 0.6157431443809883
73 [anonymised] 2016-02-12 07:55 1.0.0 men only baseline 0.5 0.5
71 p/tlen 2016-02-11 22:10 1.0.0 use "leaks" 0.5099823404915005 0.5103896257138811
72 [anonymised] 2016-01-08 09:25 1.0.0 man only baseline 0.5 0.5
82 [anonymised] 2015-12-17 08:38 1.0.0 naive bayes 0.577344603065475 N/A
46 [anonymised] 2015-12-17 07:34 1.0.0 pliki zrodlowe w odp. folderach 0.6269058114611559 0.6288407986029169
65 [anonymised] 2015-12-16 21:17 1.0.0 Dodane kody zrodlowe 0.5852779013494022 0.5719132014914806
64 [anonymised] 2015-12-16 21:09 1.0.0 uhhh, test numer 3 0.5852779013494022 0.5719132014914806
75 [anonymised] 2015-12-16 18:56 1.0.0 dominik szczeszynski - proba 2 0.5009908197516885 0.4991504224288479
74 [anonymised] 2015-12-10 09:08 1.0.0 Piotr Mizerka kobieta_czy_mezczyzna Naiwny Bayes 0.6269058114611559 0.4995044130834946
33 [anonymised] 2015-12-10 09:05 1.0.0 naive bayes by Przemysław Nowaczyk kod zrodlowy i zasoby 0.6662487699006484 0.646864822768679
81 [anonymised] 2015-12-10 09:05 1.0.0 Piotr Mizerka kobieta_czy_mezczyzna Naiwny Bayes 0.6269058114611559 N/A
32 [anonymised] 2015-12-10 08:19 1.0.0 naive bayes by Przemysław Nowaczyk 0.6662487699006484 0.646864822768679
76 [anonymised] 2015-12-10 07:48 1.0.0 dominik szczeszynski - solution 0.4977824510319354 0.49843654127531034