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 | Nikodem Pachala | 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 | s426135 | 2020-06-15 13:51 | 3.0.0 | docker lstm | 0.7229 | 0.7618 | 0.7418 | 0.8164 | 0.6750 | 25 | |
| 4 | Adam Chrzanowski | 2020-04-20 13:34 | 3.0.0 | vowpal-wabbit probabilities | 0.8340 | 0.7131 | 0.7688 | 0.8170 | 0.6650 | 20 | |
| 5 | Szymon Grad | 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 | Jakub 452101 | 2020-04-23 08:32 | 3.0.0 | ISI-33, probabilities probabilities | 0.6729 | 0.7718 | 0.7190 | 0.8080 | 0.6586 | 11 | |
| 7 | Mateusz Mucha | 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 | Mikolaj Bachorz | 2020-04-27 04:03 | 3.0.0 | v2 probabilities | 0.6846 | 0.7483 | 0.7150 | 0.8009 | 0.6482 | 74 | |
| 9 | Damian Litwin | 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 | Dominika | 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 | Lukasz Dawydzik | 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 | Dawid Majsnerowski | 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 | Eryk Sokołowski | 2020-06-09 10:58 | 3.0.0 | probability probabilities | 0.7862 | 0.7106 | 0.7465 | 0.8051 | 0.6062 | 23 | |
| 14 | Michał Maciaszek | 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 | Patryk Dolata | 2020-04-25 10:28 | 3.0.0 | bayes prob probabilities | 0.7846 | 0.7068 | 0.7436 | 0.8026 | 0.6035 | 16 | |
| 16 | Artur Dylewski | 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 | Yevheniia Tsapkova | 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 | klaganowski | 2020-04-27 14:27 | 3.0.0 | Naive Bayes (probabilities) probabilities | 0.7777 | 0.6992 | 0.7363 | 0.7968 | 0.5056 | 11 | |
| 22 | Dawid Jurkiewicz | 2020-05-27 17:42 | 3.0.0 | Stupid solution stupid baseline | 0.0000 | 0.0000 | 0.0000 | 0.6351 | 0.0000 | 1 | |
| 23 | Anna Maduzia | 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 | Bartosz | 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 | Antoni | 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 | Javier Martinez Jimenez | 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 | Mat | 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 | Hellen | 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 | Agata Buszczak | 2021-02-17 19:59 | 3.0.0 | paranormal logistic-regression pytorch-nn | 0.1729 | 0.3706 | 0.2358 | 0.5910 | 0.0000 | 2 |