Skeptic vs paranormal subreddits
Classify a reddit as either from Skeptic subreddit or one of the "paranormal" subreddits (Paranormal, UFOs, TheTruthIsHere, Ghosts, ,Glitch-in-the-Matrix, conspiracytheories). [ver. 3.0.0]
Git repo URL: git://gonito.net/paranormal-or-skeptic / Branch: master
Run git clone --single-branch git://gonito.net/paranormal-or-skeptic -b master to get the challenge data
Browse at https://gonito.net/gitlist/paranormal-or-skeptic.git/master
Leaderboard
# | submitter | when | ver. | description | test-A Recall | test-A Precision | test-A F1.0 | test-A Accuracy | test-A Likelihood | × | |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | kubapok | 2020-05-13 15:01 | 3.0.0 | roberta large finetuning 1 epoch fairseq (may be finetuned still) | 0.8117 | 0.8417 | 0.8264 | 0.8756 | 0.7525 | 2 | |
2 | [anonymized] | 2020-04-22 23:10 | 3.0.0 | Sklearn Logistic regression 1-3gram 1000iter probabilities | 0.7011 | 0.7855 | 0.7409 | 0.8210 | 0.6767 | 8 | |
3 | [anonymized] | 2020-06-15 13:51 | 3.0.0 | docker lstm | 0.7229 | 0.7618 | 0.7418 | 0.8164 | 0.6750 | 25 | |
4 | [anonymized] | 2020-04-20 13:34 | 3.0.0 | vowpal-wabbit probabilities | 0.8340 | 0.7131 | 0.7688 | 0.8170 | 0.6650 | 20 | |
5 | [anonymized] | 2020-04-27 14:45 | 3.0.0 | first test vowpal vowpal-wabbit logistic-regression hyperparam | 0.6543 | 0.7775 | 0.7106 | 0.8055 | 0.6621 | 22 | |
6 | [anonymized] | 2020-04-23 08:32 | 3.0.0 | ISI-33, probabilities probabilities | 0.6729 | 0.7718 | 0.7190 | 0.8080 | 0.6586 | 11 | |
7 | [anonymized] | 2020-04-26 14:07 | 3.0.0 | Vowpal wabbit hyperparam change vowpal-wabbit logistic-regression hyperparam | 0.6319 | 0.7984 | 0.7055 | 0.8075 | 0.6580 | 17 | |
8 | [anonymized] | 2020-04-27 04:03 | 3.0.0 | v2 probabilities | 0.6846 | 0.7483 | 0.7150 | 0.8009 | 0.6482 | 74 | |
9 | [anonymized] | 2020-04-21 21:22 | 3.0.0 | NB sklearn with probo probabilities | 0.3856 | 0.8610 | 0.5327 | 0.7531 | 0.6297 | 9 | |
10 | [anonymized] | 2020-06-05 13:35 | 3.0.0 | last-branch ready-made xgboost | 0.4426 | 0.8181 | 0.5744 | 0.7607 | 0.6237 | 14 | |
11 | [anonymized] | 2020-06-10 00:22 | 3.0.0 | improved splitting naive-bayes multinomial baseline | 0.7202 | 0.7448 | 0.7323 | 0.8078 | 0.6091 | 17 | |
12 | [anonymized] | 2020-04-21 16:44 | 3.0.0 | Add Likelihood to ready made NB probabilities | 0.7479 | 0.7173 | 0.7323 | 0.8005 | 0.6074 | 18 | |
13 | [anonymized] | 2020-06-09 10:58 | 3.0.0 | probability probabilities | 0.7862 | 0.7106 | 0.7465 | 0.8051 | 0.6062 | 23 | |
14 | [anonymized] | 2020-05-18 13:56 | 3.0.0 | improved naive-bayes multinomial baseline | 0.7862 | 0.7106 | 0.7465 | 0.8051 | 0.6062 | 18 | |
15 | [anonymized] | 2020-04-25 10:28 | 3.0.0 | bayes prob probabilities | 0.7846 | 0.7068 | 0.7436 | 0.8026 | 0.6035 | 16 | |
16 | [anonymized] | 2020-06-03 16:27 | 3.0.0 | ISI52 logistic-regression word2vec | 0.6223 | 0.7410 | 0.6765 | 0.7828 | 0.5832 | 14 | |
17 | [anonymized] | 2020-06-08 16:50 | 3.0.0 | knn tf knn tf | 0.2234 | 0.8434 | 0.3532 | 0.7015 | 0.5623 | 45 | |
18 | [anonymized] | 2020-05-18 14:57 | 3.0.0 | knn-tfidf-v5 knn tf-idf | 0.3420 | 0.8654 | 0.4903 | 0.7405 | 0.5615 | 14 | |
19 | [anonymized] | 2020-06-07 13:54 | 3.0.0 | tf idf solution knn tf-idf | 0.1319 | 0.9118 | 0.2305 | 0.6786 | 0.5547 | 11 | |
20 | p/tlen | 2020-04-20 13:18 | 3.0.0 | To probabilities null-model baseline | 0.1048 | 0.8914 | 0.1875 | 0.6687 | 0.5282 | 4 | |
21 | [anonymized] | 2020-04-27 14:27 | 3.0.0 | Naive Bayes (probabilities) probabilities | 0.7777 | 0.6992 | 0.7363 | 0.7968 | 0.5056 | 11 | |
22 | [anonymized] | 2020-05-27 17:42 | 3.0.0 | Stupid solution stupid baseline | 0.0000 | 0.0000 | 0.0000 | 0.6351 | 0.0000 | 1 | |
23 | [anonymized] | 2020-06-23 22:59 | 3.0.0 | svm ready-made svm | 0.6021 | 0.7685 | 0.6752 | 0.7886 | 0.0000 | 14 | |
24 | [anonymized] | 2020-06-30 22:44 | 3.0.0 | Upload files to 'test-A' self-made linear-regression gradient-descent | 0.3362 | 0.5351 | 0.4129 | 0.6512 | 0.0000 | 11 | |
25 | [anonymized] | 2020-12-16 11:46 | 3.0.0 | working logistic-regression pytorch-nn | 0.1729 | 0.3714 | 0.2359 | 0.5914 | 0.0000 | 1 | |
26 | [anonymized] | 2020-06-09 20:02 | 3.0.0 | Use Linear Regression V6 self-made linear-regression gradient-descent | 0.4803 | 0.5953 | 0.5316 | 0.6912 | 0.0000 | 44 | |
27 | [anonymized] | 2020-05-04 13:16 | 3.0.0 | linear regression gradient descent self-made linear-regression gradient-descent | 0.3660 | 0.7845 | 0.4991 | 0.7319 | 0.0000 | 22 | |
28 | [anonymized] | 2020-05-27 17:58 | 3.0.0 | Stupid solution stupid baseline | 0.0000 | 0.0000 | 0.0000 | 0.6351 | 0.0000 | 2 | |
29 | [anonymized] | 2020-12-16 10:53 | 3.0.0 | 4 iteracje | 0.1691 | 0.3693 | 0.2320 | 0.5914 | 0.0000 | 5 | |
30 | [anonymized] | 2020-12-16 08:41 | 3.0.0 | Moje genialne rozwiazanie logistic-regression pytorch-nn | 0.0436 | 0.4000 | 0.0787 | 0.6271 | 0.0000 | 3 | |
31 | [anonymized] | 2021-02-18 20:19 | 3.0.0 | update + out, final logistic-regression pytorch-nn | 0.1734 | 0.3730 | 0.2367 | 0.5920 | 0.0000 | 2 | |
32 | [anonymized] | 2021-02-17 19:59 | 3.0.0 | paranormal logistic-regression pytorch-nn | 0.1729 | 0.3706 | 0.2358 | 0.5910 | 0.0000 | 2 | |
33 | Marcin Kostrzewski | 2022-05-10 22:01 | 3.0.0 | Add predictions naive-bayes | 0.3718 | 0.7327 | 0.4933 | 0.7213 | 0.0000 | 1 | |
34 | Martyna Druminska | 2022-05-28 13:44 | 3.0.0 | my brilliant self-made neural-network word2vec pytorch-nn | 0.4287 | 0.7133 | 0.5355 | 0.7286 | 0.0000 | 3 | |
35 | s444417 | 2022-05-18 08:59 | 3.0.0 | fix output naive-bayes | 0.6213 | 0.5458 | 0.5811 | 0.6731 | 0.0000 | 7 | |
36 | s443930 | 2022-06-22 21:37 | 3.0.0 | s443930 transformer | 0.4723 | 0.6794 | 0.5573 | 0.7261 | 0.0000 | 4 | |
37 | s409771 | 2022-05-25 20:59 | 3.0.0 | first solution neural-network word2vec | 0.5181 | 0.7356 | 0.6080 | 0.7562 | 0.0000 | 2 | |
38 | s478815 | 2022-06-14 22:10 | 3.0.0 | 478815 neural-network word2vec | 0.5830 | 0.7282 | 0.6476 | 0.7684 | 0.0000 | 3 | |
39 | s444383 | 2022-09-23 13:43 | 3.0.0 | geval | 0.7633 | 0.6218 | 0.6853 | 0.7442 | 0.0000 | 1 | |
40 | Jakub Eichner | 2022-06-22 08:12 | 3.0.0 | s478874 transformer | 0.8027 | 0.6087 | 0.6924 | 0.7397 | 0.0000 | 6 | |
41 | ked | 2022-05-24 21:35 | 3.0.0 | s449288 - simple NN with GloVe vectors neural-network word2vec | 0.6096 | 0.6945 | 0.6493 | 0.7597 | 0.0000 | 3 | |
42 | Cezary | 2022-05-25 21:10 | 3.0.0 | s470623 neural-network word2vec | 0.4819 | 0.7067 | 0.5731 | 0.7380 | 0.0000 | 1 | |
43 | [anonymized] | 2022-05-27 15:37 | 3.0.0 | 478841 results neural-network word2vec | 0.6037 | 0.7143 | 0.6544 | 0.7673 | 0.0000 | 3 | |
44 | Jakub | 2022-06-08 09:09 | 3.0.0 | s434624 neural-network word2vec | 0.0229 | 0.4433 | 0.0435 | 0.6330 | 0.0000 | 1 | |
45 | [anonymized] | 2022-05-24 09:15 | 3.0.0 | s478831 neural-network word2vec | 0.6144 | 0.6082 | 0.6113 | 0.7149 | 0.0000 | 1 | |
46 | 444498 | 2022-05-11 21:45 | 3.0.0 | s444498 naive-bayes | 0.3223 | 0.7690 | 0.4543 | 0.7174 | 0.0000 | 2 | |
47 | Mikołaj Pokrywka | 2022-05-24 21:43 | 3.0.0 | s444463 neural-network word2vec | 0.5197 | 0.7797 | 0.6237 | 0.7712 | 0.0000 | 2 | |
48 | s444517 | 2022-05-23 15:13 | 3.0.0 | s444517 - PyTorch feed forward neural network self-made neural-network word2vec | 0.3973 | 0.7155 | 0.5109 | 0.7224 | 0.0000 | 2 | |
49 | s478855 | 2022-06-22 11:35 | 3.0.0 | s478855 - correct outs transformer | 0.7234 | 0.7572 | 0.7399 | 0.8144 | 0.0000 | 3 | |
50 | s478840 | 2022-06-21 21:00 | 3.0.0 | s478840 transformer | 0.6468 | 0.6441 | 0.6454 | 0.7407 | 0.0000 | 5 | |
51 | s478846 | 2022-05-24 09:45 | 3.0.0 | First solution neural-network word2vec | 0.6766 | 0.7374 | 0.7057 | 0.7941 | 0.0000 | 1 | |
52 | s444356 | 2022-05-09 18:46 | 3.0.0 | s444356 naive-bayes | 0.3718 | 0.7327 | 0.4933 | 0.7213 | 0.0000 | 1 | |
53 | s444501 | 2022-05-10 14:11 | 3.0.0 | s444501 naive-bayes | 0.2968 | 0.8242 | 0.4364 | 0.7203 | 0.0000 | 1 | |
54 | Kamil Guttmann | 2022-05-23 19:32 | 3.0.0 | s444380 simple nn + word2vec neural-network word2vec | 0.4676 | 0.7343 | 0.5713 | 0.7440 | 0.0000 | 3 | |
55 | [anonymized] | 2022-05-08 20:10 | 3.0.0 | 444421 naive-bayes | 0.2734 | 0.7823 | 0.4052 | 0.7071 | 0.0000 | 1 | |
56 | s444465 | 2022-05-23 14:54 | 3.0.0 | s444465 neural-network word2vec | 0.6059 | 0.7363 | 0.6647 | 0.7770 | 0.0000 | 2 | |
57 | s444018 | 2022-05-10 22:21 | 3.0.0 | s444018 naive-bayes self-made | 0.3835 | 0.8857 | 0.5353 | 0.7570 | 0.0000 | 2 | |
58 | Adam Wojdyła | 2022-06-01 21:08 | 3.0.0 | 4444507 neural-network word2vec | 0.4654 | 0.7184 | 0.5649 | 0.7384 | 0.0000 | 4 | |
59 | [anonymized] | 2022-06-06 21:43 | 3.0.0 | out dev neural-network word2vec | 0.5229 | 0.7352 | 0.6111 | 0.7572 | 0.0000 | 1 |