Mushroom classification challenge
Predict whether the mushroom is edible (e) or poisonous (p). [ver. 1.0.0]
This is a long list of all submissions, if you want to see only the best, click leaderboard.
| # | submitter | when | ver. | description | dev-0 Accuracy | test-A Accuracy | |
|---|---|---|---|---|---|---|---|
| 123 | [anonymized] | 2019-06-18 12:29 | 1.0.0 | grzyby logistyczne ready-made logistic-regression | 1.0000 | 1.0000 | |
| 122 | [anonymized] | 2019-06-16 08:47 | 1.0.0 | grzyby ready ready-made logistic-regression | 1.0000 | 1.0000 | |
| 121 | [anonymized] | 2019-06-16 08:33 | 1.0.0 | mushrooms ready-made ready-made logistic-regression | 1.0000 | 1.0000 | |
| 120 | [anonymized] | 2019-06-16 08:16 | 1.0.0 | grzyby ready-made logistic-regression | 1.0000 | 1.0000 | |
| 119 | [anonymized] | 2019-06-15 14:33 | 1.0.0 | jadalne vs niejadalne ready-made logistic-regression | 1.0000 | 1.0000 | |
| 118 | [anonymized] | 2019-06-12 19:30 | 1.0.0 | mushroom ready made ready-made logistic-regression | 1.0000 | 1.0000 | |
| 117 | [anonymized] | 2019-06-10 20:48 | 1.0.0 | knn knn | 1.0000 | 1.0000 | |
| 245 | [anonymized] | 2019-06-10 20:39 | 1.0.0 | change to naive bayes naive-bayes | 0.9268 | 0.9389 | |
| 116 | [anonymized] | 2019-06-10 20:28 | 1.0.0 | logistic reg logistic-regression | 1.0000 | 1.0000 | |
| 115 | [anonymized] | 2019-06-10 12:19 | 1.0.0 | grzybki knn knn | 1.0000 | 1.0000 | |
| 198 | [anonymized] | 2019-06-10 12:17 | 1.0.0 | Naiwne Grzybki naive-bayes | 0.9596 | 0.9608 | |
| 114 | [anonymized] | 2019-06-05 14:05 | 1.0.0 | grzybki ready logistic-regression | 1.0000 | 1.0000 | |
| 113 | [anonymized] | 2019-06-03 14:46 | 1.0.0 | Logistic regression ready-made logistic-regression | 1.0000 | 1.0000 | |
| 112 | [anonymized] | 2019-06-02 16:03 | 1.0.0 | ready-made-mushrooms ready-made logistic-regression | 1.0000 | 1.0000 | |
| 235 | [anonymized] | 2019-06-02 08:14 | 1.0.0 | Mushrooms - rozwiązanie ready-made ready-made logistic-regression | 0.9394 | 0.9469 | |
| 111 | [anonymized] | 2019-06-01 23:00 | 1.0.0 | mushrooms1 ready-made logistic-regression | 1.0000 | 1.0000 | |
| 110 | [anonymized] | 2019-06-01 21:18 | 1.0.0 | jadalne vs niejadalne ready-made logistic-regression | 1.0000 | 1.0000 | |
| 109 | [anonymized] | 2019-06-01 20:00 | 1.0.0 | grzyby self-made logistic-regression | 1.0000 | 1.0000 | |
| 108 | [anonymized] | 2019-06-01 17:47 | 1.0.0 | knn knn | 1.0000 | 1.0000 | |
| 309 | [anonymized] | 2019-06-01 17:35 | 1.0.0 | bayes naive-bayes umz-2019-challenge | 0.8093 | 0.8189 | |
| 107 | [anonymized] | 2019-06-01 14:56 | 1.0.0 | grzyby | 1.0000 | 1.0000 | |
| 197 | [anonymized] | 2019-06-01 14:37 | 1.0.0 | grzyby10 ready-made logistic-regression | 0.9722 | 0.9654 | |
| 338 | [anonymized] | 2019-06-01 08:22 | 1.0.0 | logistic regression ready made ready-made logistic-regression | 0.4230 | 0.4464 | |
| 106 | [anonymized] | 2019-05-31 15:57 | 1.0.0 | grzybki ready-made logistic-regression | 1.0000 | 1.0000 | |
| 105 | [anonymized] | 2019-05-28 14:02 | 1.0.0 | Grzyb logistic-regression | 1.0000 | 1.0000 | |
| 265 | [anonymized] | 2019-05-28 14:01 | 1.0.0 | grzbkiKNN knn | 0.9205 | 0.9123 | |
| 104 | [anonymized] | 2019-05-26 11:50 | 1.0.0 | add load data | 1.0000 | 1.0000 | |
| 103 | [anonymized] | 2019-05-22 21:02 | 1.0.0 | Mushrooms logistic regression ready-made logistic-regression | 1.0000 | 1.0000 | |
| 102 | [anonymized] | 2019-05-22 17:15 | 1.0.0 | regresja log na grzybach ready-made logistic-regression | 1.0000 | 1.0000 | |
| 310 | [anonymized] | 2019-05-22 14:26 | 1.0.0 | logistic-regression-selfmade self-made logistic-regression | 0.7247 | 0.7532 | |
| 101 | [anonymized] | 2019-05-22 09:11 | 1.0.0 | logistic-regression-readymade ready-made logistic-regression | 1.0000 | 1.0000 | |
| 100 | [anonymized] | 2019-05-22 06:58 | 1.0.0 | grzybkiknn knn | 1.0000 | 1.0000 | |
| 99 | [anonymized] | 2019-05-20 12:26 | 1.0.0 | KNN MUSH knn | N/A | 1.0000 | |
| 98 | [anonymized] | 2019-05-19 20:42 | 1.0.0 | many features knn | 1.0000 | 1.0000 | |
| 97 | [anonymized] | 2019-05-19 20:05 | 1.0.0 | knn knn | 1.0000 | 1.0000 | |
| 96 | [anonymized] | 2019-05-19 19:52 | 1.0.0 | KNN knn | 1.0000 | 1.0000 | |
| 95 | [anonymized] | 2019-05-19 19:13 | 1.0.0 | Zadanie06 knn | 1.0000 | 1.0000 | |
| 363 | [anonymized] | 2019-05-19 17:21 | 1.0.0 | mushrooms knn knn umz-2019-challenge | N/A | N/A | |
| 94 | [anonymized] | 2019-05-19 16:27 | 1.0.0 | mushrooms 3 commit ready-made logistic-regression | 1.0000 | 1.0000 | |
| 362 | [anonymized] | 2019-05-19 16:19 | 1.0.0 | mushrooms 1st commit | 1.0000 | N/A | |
| 93 | [anonymized] | 2019-05-19 13:25 | 1.0.0 | LogicalRegression 1.3 ready-made logistic-regression | 1.0000 | 1.0000 | |
| 92 | [anonymized] | 2019-05-19 13:02 | 1.0.0 | kraina-grzbow ready-made logistic-regression | 1.0000 | 1.0000 | |
| 91 | [anonymized] | 2019-05-17 17:06 | 1.0.0 | knn knn umz-2019-challenge | 1.0000 | 1.0000 | |
| 90 | [anonymized] | 2019-05-17 10:51 | 1.0.0 | knnka knn umz-2019-challenge | 1.0000 | 1.0000 | |
| 89 | [anonymized] | 2019-05-17 10:18 | 1.0.0 | knn knn | 1.0000 | 1.0000 | |
| 178 | [anonymized] | 2019-05-16 13:36 | 1.0.0 | swojaNazwa knn | 0.9874 | 0.9850 | |
| 157 | [anonymized] | 2019-05-16 13:29 | 1.0.0 | grzybki knn knn | 0.9962 | 0.9931 | |
| 169 | [anonymized] | 2019-05-16 13:29 | 1.0.0 | mowiemowie knn | 0.9949 | 0.9885 | |
| 88 | [anonymized] | 2019-05-15 07:21 | 1.0.0 | KNN knn | 1.0000 | 1.0000 | |
| 166 | [anonymized] | 2019-05-14 13:54 | 1.0.0 | mushroomsLog logistic-regression | 0.9949 | 0.9919 | |
| 87 | [anonymized] | 2019-05-14 12:58 | 1.0.0 | w lesie knn | 1.0000 | 1.0000 | |
| 301 | [anonymized] | 2019-05-14 12:40 | 1.0.0 | naive mashrooms naive-bayes | 0.9066 | 0.9100 | |
| 151 | [anonymized] | 2019-05-14 12:28 | 1.0.0 | knn knn | 0.9949 | 0.9954 | |
| 86 | [anonymized] | 2019-05-14 12:28 | 1.0.0 | knnashrooms knn | 1.0000 | 1.0000 | |
| 85 | [anonymized] | 2019-05-14 12:19 | 1.0.0 | KNeighborsClassifier knn | 1.0000 | 1.0000 | |
| 145 | [anonymized] | 2019-05-14 12:16 | 1.0.0 | knn knn | 1.0000 | 0.9977 | |
| 142 | [anonymized] | 2019-05-14 12:07 | 1.0.0 | knn knn | 0.9975 | 0.9988 | |
| 84 | [anonymized] | 2019-05-14 12:01 | 1.0.0 | kryminalni knn | 1.0000 | 1.0000 | |
| 83 | [anonymized] | 2019-05-14 11:51 | 1.0.0 | lustereczko naive-bayes | 1.0000 | 1.0000 | |
| 82 | [anonymized] | 2019-05-13 16:35 | 1.0.0 | knn knn | 1.0000 | 1.0000 | |
| 300 | [anonymized] | 2019-05-13 16:27 | 1.0.0 | bayes1 naive-bayes | 0.9066 | 0.9100 | |
| 141 | [anonymized] | 2019-05-13 11:52 | 1.0.0 | knn solution for k=3 knn | 0.9962 | 0.9988 | |
| 361 | [anonymized] | 2019-05-13 11:48 | 1.0.0 | knn solution for k=3 | 0.9975 | N/A | |
| 81 | [anonymized] | 2019-05-12 22:49 | 1.0.0 | UMZ2019-06 - K nearest neighbors knn umz-2019-challenge | 1.0000 | 1.0000 | |
| 80 | [anonymized] | 2019-05-12 22:04 | 1.0.0 | psylocybinka knn | 1.0000 | 1.0000 | |
| 299 | [anonymized] | 2019-05-12 21:32 | 1.0.0 | grzybki jeszcze raz naive-bayes | 0.9066 | 0.9100 | |
| 192 | [anonymized] | 2019-05-12 20:53 | 1.0.0 | naive bayes naive-bayes | 0.9886 | 0.9792 | |
| 360 | [anonymized] | 2019-05-12 19:43 | 1.0.0 | mushroom Naive Bayes naive-bayes umz-2019-challenge | N/A | N/A | |
| 298 | [anonymized] | 2019-05-12 17:06 | 1.0.0 | Naive Bayes naive-bayes | 0.9066 | 0.9100 | |
| 246 | [anonymized] | 2019-05-12 16:39 | 1.0.0 | naive bayes naive-bayes | 0.9280 | 0.9343 | |
| 359 | [anonymized] | 2019-05-12 16:29 | 1.0.0 | naive bayes | N/A | N/A | |
| 297 | [anonymized] | 2019-05-11 21:08 | 1.0.0 | Zadanie05 naive-bayes | 0.9066 | 0.9100 | |
| 165 | [anonymized] | 2019-05-11 13:24 | 1.0.0 | baybay naive-bayes umz-2019-challenge | 0.9924 | 0.9919 | |
| 263 | [anonymized] | 2019-05-11 13:03 | 1.0.0 | UMZ2019-05 - Naive Bayes naive-bayes | 0.9230 | 0.9158 | |
| 184 | [anonymized] | 2019-05-10 12:34 | 1.0.0 | NB naive-bayes | 0.9899 | 0.9815 | |
| 164 | [anonymized] | 2019-05-10 11:37 | 1.0.0 | Naive Bayes with dropped columns naive-bayes | 0.9949 | 0.9919 | |
| 173 | [anonymized] | 2019-05-09 21:31 | 1.0.0 | Naiwny Bayes naive-bayes | 0.9924 | 0.9873 | |
| 251 | [anonymized] | 2019-05-09 11:27 | 1.0.0 | naiwne grzybki naive-bayes | 0.9242 | 0.9273 | |
| 79 | [anonymized] | 2019-05-09 10:00 | 1.0.0 | my solution knn knn | 1.0000 | 1.0000 | |
| 78 | [anonymized] | 2019-05-08 20:09 | 1.0.0 | knn musrooms knn | 1.0000 | 1.0000 | |
| 296 | [anonymized] | 2019-05-08 19:45 | 1.0.0 | Naive bayes naive-bayes | 0.9066 | 0.9100 | |
| 262 | [anonymized] | 2019-05-07 23:18 | 1.0.0 | Bayes grzyby 1 naive-bayes | 0.9230 | 0.9158 | |
| 140 | [anonymized] | 2019-05-07 23:03 | 1.0.0 | Grzyby KNN 1 knn | 0.9987 | 0.9988 | |
| 195 | [anonymized] | 2019-05-07 19:48 | 1.0.0 | naive bayes naive-bayes | 0.9646 | 0.9689 | |
| 77 | [anonymized] | 2019-05-07 19:38 | 1.0.0 | knn dla 3 knn umz-2019-challenge | 1.0000 | 1.0000 | |
| 330 | [anonymized] | 2019-05-07 19:33 | 1.0.0 | knn dla 3 | 0.4987 | 0.5052 | |
| 319 | [anonymized] | 2019-05-07 19:31 | 1.0.0 | knn dla 3 | 0.5909 | 0.6113 | |
| 332 | [anonymized] | 2019-05-07 19:30 | 1.0.0 | knn dla 3 | 1.0000 | 0.4948 | |
| 76 | [anonymized] | 2019-05-07 15:07 | 1.0.0 | rozwiazanie 07052019 1706 knn | 1.0000 | 1.0000 | |
| 295 | [anonymized] | 2019-05-07 15:00 | 1.0.0 | rozwiazanie 07052019 1659 naive-bayes | 0.9066 | 0.9100 | |
| 163 | [anonymized] | 2019-05-07 14:17 | 1.0.0 | grzybki naive-bayes | 0.9949 | 0.9919 | |
| 294 | [anonymized] | 2019-05-07 14:10 | 1.0.0 | solution with naive bayes ready-made naive-bayes umz-2019-challenge | 0.9066 | 0.9100 | |
| 75 | [anonymized] | 2019-05-07 14:08 | 1.0.0 | solution with KNN ready-made knn umz-2019-challenge | 1.0000 | 1.0000 | |
| 177 | [anonymized] | 2019-05-07 14:04 | 1.0.0 | MojeGrzybki naive-bayes | 0.9874 | 0.9850 | |
| 162 | [anonymized] | 2019-05-07 14:03 | 1.0.0 | bayess.py naive-bayes | 0.9949 | 0.9919 | |
| 156 | [anonymized] | 2019-05-07 13:59 | 1.0.0 | naiwne rozwiazanie naive-bayes | 0.9962 | 0.9931 | |
| 168 | [anonymized] | 2019-05-07 13:59 | 1.0.0 | dobrywynik naive-bayes | 0.9949 | 0.9885 | |
| 74 | [anonymized] | 2019-05-07 13:24 | 1.0.0 | grzybkiKNN knn | 1.0000 | 1.0000 | |
| 194 | [anonymized] | 2019-05-07 13:22 | 1.0.0 | grzybkiNB naive-bayes | 0.9646 | 0.9689 | |
| 73 | [anonymized] | 2019-05-07 13:19 | 1.0.0 | grzybki logic logistic-regression | 1.0000 | 1.0000 | |
| 72 | [anonymized] | 2019-05-07 13:18 | 1.0.0 | grzybki logistic-regression | 1.0000 | 1.0000 | |
| 139 | [anonymized] | 2019-05-07 13:16 | 1.0.0 | grzybki nb naive-bayes | 1.0000 | 0.9988 | |
| 138 | [anonymized] | 2019-05-07 13:05 | 1.0.0 | GaussianNB naive-bayes | 0.9987 | 0.9988 | |
| 261 | [anonymized] | 2019-05-07 12:44 | 1.0.0 | bayes naive-bayes | 0.9230 | 0.9158 | |
| 175 | [anonymized] | 2019-05-07 12:41 | 1.0.0 | GaussianNB naive-bayes | 0.9912 | 0.9862 | |
| 71 | [anonymized] | 2019-05-07 12:40 | 1.0.0 | knn solution knn | 1.0000 | 1.0000 | |
| 191 | [anonymized] | 2019-05-07 12:40 | 1.0.0 | GaussianNB naive-bayes | 0.9886 | 0.9792 | |
| 70 | [anonymized] | 2019-05-07 12:32 | 1.0.0 | grzybyKNN knn | 1.0000 | 1.0000 | |
| 264 | [anonymized] | 2019-05-07 12:23 | 1.0.0 | GrzybkiK nearest neighbors knn | 0.9205 | 0.9123 | |
| 69 | [anonymized] | 2019-05-07 12:16 | 1.0.0 | naive bayes naive-bayes | 0.9596 | 1.0000 | |
| 306 | [anonymized] | 2019-05-07 12:15 | 1.0.0 | GrzybkiNaive naive-bayes | 0.8826 | 0.8893 | |
| 190 | [anonymized] | 2019-05-07 12:15 | 1.0.0 | my solution naive-bayes | 0.9886 | 0.9792 | |
| 137 | [anonymized] | 2019-05-07 12:08 | 1.0.0 | grzybyNB naive-bayes | 1.0000 | 0.9988 | |
| 172 | [anonymized] | 2019-05-07 12:05 | 1.0.0 | grzybyKNN knn | 0.9924 | 0.9873 | |
| 68 | [anonymized] | 2019-05-06 22:31 | 1.0.0 | neighbourhood knn | 1.0000 | 1.0000 | |
| 67 | [anonymized] | 2019-05-06 12:58 | 1.0.0 | Mushroom KNN knn umz-2019-challenge | 1.0000 | 1.0000 | |
| 308 | [anonymized] | 2019-05-06 12:32 | 1.0.0 | NB naive-bayes | 0.8093 | 0.8189 | |
| 171 | [anonymized] | 2019-05-06 10:18 | 1.0.0 | Naive Bayes, Mushroom challenge naive-bayes umz-2019-challenge | 0.9760 | 0.9873 | |
| 66 | [anonymized] | 2019-05-06 10:04 | 1.0.0 | Regresja logistyczna, Mushroom challenge logistic-regression umz-2019-challenge | N/A | 1.0000 | |
| 293 | [anonymized] | 2019-05-04 19:59 | 1.0.0 | BAY-corrected naive-bayes | 0.9066 | 0.9100 | |
| 65 | [anonymized] | 2019-05-03 18:26 | 1.0.0 | m u s h r o o m s knn | 1.0000 | 1.0000 | |
| 292 | [anonymized] | 2019-05-03 18:19 | 1.0.0 | his name was Thomas Bayes naive-bayes | 0.9066 | 0.9100 | |
| 64 | [anonymized] | 2019-05-03 15:19 | 1.0.0 | KNN oh that is good knn | 1.0000 | 1.0000 | |
| 291 | [anonymized] | 2019-05-03 15:11 | 1.0.0 | Bayes was a good man naive-bayes | 0.9066 | 0.9100 | |
| 148 | [anonymized] | 2019-04-30 21:54 | 1.0.0 | 7 cech | 1.0000 | 0.9965 | |
| 63 | [anonymized] | 2019-04-30 21:47 | 1.0.0 | first solution logistic-regression umz-2019-challenge | 1.0000 | 1.0000 | |
| 233 | [anonymized] | 2019-04-30 19:30 | 1.0.0 | Logistic Regression logistic-regression | 0.9432 | 0.9481 | |
| 62 | [anonymized] | 2019-04-30 19:23 | 1.0.0 | check logistic-regression | 1.0000 | 1.0000 | |
| 244 | [anonymized] | 2019-04-30 16:14 | 1.0.0 | Zadanie04 logistic-regression | 0.9394 | 0.9423 | |
| 346 | [anonymized] | 2019-04-30 15:55 | 1.0.0 | Zadanie04 | 0.0000 | 0.0000 | |
| 161 | [anonymized] | 2019-04-30 14:18 | 1.0.0 | diamencik logistic-regression | 0.9949 | 0.9919 | |
| 304 | [anonymized] | 2019-04-30 14:07 | 1.0.0 | Grzybki123 logistic-regression | 0.9167 | 0.9066 | |
| 61 | [anonymized] | 2019-04-30 14:07 | 1.0.0 | Najnowsza wersja grzybów logistic-regression | 1.0000 | 1.0000 | |
| 136 | [anonymized] | 2019-04-30 13:08 | 1.0.0 | afcnr1 logistic-regression | 1.0000 | 0.9988 | |
| 160 | [anonymized] | 2019-04-30 13:07 | 1.0.0 | my solutioon logistic-regression | 0.9949 | 0.9919 | |
| 159 | [anonymized] | 2019-04-30 12:41 | 1.0.0 | taktak logistic-regression | 0.9949 | 0.9919 | |
| 170 | [anonymized] | 2019-04-30 12:39 | 1.0.0 | grzybyNB naive-bayes | 0.9924 | 0.9873 | |
| 183 | [anonymized] | 2019-04-30 12:38 | 1.0.0 | grzyb 3 knn | 0.9836 | 0.9815 | |
| 260 | [anonymized] | 2019-04-30 12:34 | 1.0.0 | pieczarki vol. 3 knn | 0.9268 | 0.9158 | |
| 307 | [anonymized] | 2019-04-30 12:31 | 1.0.0 | grzyb 2 naive-bayes | 0.8295 | 0.8304 | |
| 60 | [anonymized] | 2019-04-30 12:31 | 1.0.0 | my mushrooms logistic-regression knn | 1.0000 | 1.0000 | |
| 259 | [anonymized] | 2019-04-30 12:29 | 1.0.0 | pieczarki vol. 2 naive-bayes | 0.9268 | 0.9158 | |
| 59 | [anonymized] | 2019-04-30 12:23 | 1.0.0 | grzyby logistic-regression | 1.0000 | 1.0000 | |
| 58 | [anonymized] | 2019-04-30 12:09 | 1.0.0 | grzybki KNN knn | 1.0000 | 1.0000 | |
| 57 | [anonymized] | 2019-04-30 12:05 | 1.0.0 | mashed mashrooms logistic-regression | 1.0000 | 1.0000 | |
| 189 | [anonymized] | 2019-04-30 12:01 | 1.0.0 | grzybki NB naive-bayes | 0.9886 | 0.9792 | |
| 56 | [anonymized] | 2019-04-30 11:54 | 1.0.0 | grzybki logistic-regression | 1.0000 | 1.0000 | |
| 55 | [anonymized] | 2019-04-30 11:35 | 1.0.0 | knn knn | 1.0000 | 1.0000 | |
| 188 | [anonymized] | 2019-04-30 11:13 | 1.0.0 | NB naive-bayes | 0.9886 | 0.9792 | |
| 135 | Artur Nowakowski | 2019-04-30 09:17 | 1.0.0 | KNN knn | 1.0000 | 0.9988 | |
| 54 | [anonymized] | 2019-04-30 08:30 | 1.0.0 | rozwiazanie na wiele zmiennych | 1.0000 | 1.0000 | |
| 53 | [anonymized] | 2019-04-29 19:09 | 1.0.0 | logistyczne grzybki v1 logistic-regression | 1.0000 | 1.0000 | |
| 328 | [anonymized] | 2019-04-29 13:17 | 1.0.0 | simple regression logistic-regression | N/A | 0.5110 | |
| 290 | [anonymized] | 2019-04-29 12:45 | 1.0.0 | Naive Bayes naive-bayes | 0.9066 | 0.9100 | |
| 155 | [anonymized] | 2019-04-29 12:30 | 1.0.0 | Gaussian naive bayes naive-bayes python scikit-learn umz-2019-challenge | N/A | 0.9931 | |
| 52 | [anonymized] | 2019-04-29 12:20 | 1.0.0 | 3nn, 5columns python scikit-learn knn umz-2019-challenge | N/A | 1.0000 | |
| 51 | [anonymized] | 2019-04-29 12:20 | 1.0.0 | knn knn | 1.0000 | 1.0000 | |
| 358 | [anonymized] | 2019-04-29 12:16 | 1.0.0 | mushrooms logisticreg logistic-regression umz-2019-challenge | N/A | N/A | |
| 50 | [anonymized] | 2019-04-29 09:21 | 1.0.0 | knn knn | 1.0000 | 1.0000 | |
| 49 | [anonymized] | 2019-04-28 20:36 | 1.0.0 | Logistic regression using OneHotEncoder logistic-regression | 1.0000 | 1.0000 | |
| 237 | [anonymized] | 2019-04-28 20:29 | 1.0.0 | Logistic regression with enc.transform | 0.9432 | 0.9458 | |
| 48 | [anonymized] | 2019-04-28 16:14 | 1.0.0 | 7param logreg python logistic-regression scikit-learn umz-2019-challenge | N/A | 1.0000 | |
| 47 | [anonymized] | 2019-04-28 15:08 | 1.0.0 | Logistic regression logistic-regression umz-2019-challenge | 1.0000 | 1.0000 | |
| 232 | [anonymized] | 2019-04-28 14:52 | 1.0.0 | My solution logistic-regression | 0.9432 | 0.9481 | |
| 46 | [anonymized] | 2019-04-27 19:16 | 1.0.0 | KNN solution knn | 1.0000 | 1.0000 | |
| 289 | [anonymized] | 2019-04-27 16:09 | 1.0.0 | Naive Bayes naive-bayes | 0.9066 | 0.9100 | |
| 242 | [anonymized] | 2019-04-27 16:08 | 1.0.0 | logistic regression logistic-regression | 0.9407 | 0.9435 | |
| 45 | [anonymized] | 2019-04-26 09:27 | 1.0.0 | logistic regression logistic-regression | 1.0000 | 1.0000 | |
| 44 | [anonymized] | 2019-04-25 20:34 | 1.0.0 | Logistic regression logistic-regression | 1.0000 | 1.0000 | |
| 333 | [anonymized] | 2019-04-25 20:22 | 1.0.0 | jeszcze jedna kolumna | 1.0000 | 0.4925 | |
| 134 | [anonymized] | 2019-04-25 20:21 | 1.0.0 | jeszcze jedna kolumna logistic-regression | 1.0000 | 0.9988 | |
| 340 | [anonymized] | 2019-04-25 20:20 | 1.0.0 | jeszcze jedna kolumna | 1.0000 | 0.3091 | |
| 339 | [anonymized] | 2019-04-25 20:17 | 1.0.0 | rozwiazanie kilka kolumn usuniete | 1.0000 | 0.3945 | |
| 327 | [anonymized] | 2019-04-25 19:28 | 1.0.0 | Pierwsza proba logistic-regression | 0.5303 | 0.5213 | |
| 43 | [anonymized] | 2019-04-25 16:06 | 1.0.0 | my solution logistic-regression | 1.0000 | 1.0000 | |
| 231 | [anonymized] | 2019-04-25 15:53 | 1.0.0 | rozwiazanie dla wielu cech logistic-regression | 0.9369 | 0.9481 | |
| 311 | [anonymized] | 2019-04-25 15:18 | 1.0.0 | rozwiazanie dla jednej zmiennej | 0.7740 | 0.7463 | |
| 303 | [anonymized] | 2019-04-25 15:17 | 1.0.0 | RozwiazanieGrzybki logistic-regression | 0.9167 | 0.9066 | |
| 133 | [anonymized] | 2019-04-25 14:13 | 1.0.0 | sezon na pieczarki logistic-regression | 1.0000 | 0.9988 | |
| 42 | [anonymized] | 2019-04-25 14:12 | 1.0.0 | Dzień Dobry logistic-regression umz-2019-challenge | 1.0000 | 1.0000 | |
| 41 | [anonymized] | 2019-04-25 01:17 | 1.0.0 | sąsiedzi knn umz-2019-challenge | 1.0000 | 1.0000 | |
| 187 | [anonymized] | 2019-04-25 01:11 | 1.0.0 | naiwniaczek naive-bayes | 0.9886 | 0.9792 | |
| 40 | [anonymized] | 2019-04-25 00:58 | 1.0.0 | atlas grzybów logistic-regression umz-2019-challenge | 1.0000 | 1.0000 | |
| 39 | [anonymized] | 2019-04-24 10:45 | 1.0.0 | finished knn knn | 1.0000 | 1.0000 | |
| 230 | [anonymized] | 2019-04-24 09:24 | 1.0.0 | UMZ2019-04 - Logistic regression logistic-regression | 0.9369 | 0.9481 | |
| 288 | [anonymized] | 2019-04-24 08:33 | 1.0.0 | finished naive bayes naive-bayes | 0.9066 | 0.9100 | |
| 38 | [anonymized] | 2019-04-24 02:26 | 1.0.0 | RogLeg logistic-regression | 1.0000 | 1.0000 | |
| 37 | [anonymized] | 2019-04-21 19:56 | 1.0.0 | K nearest neighbors knn | 1.0000 | 1.0000 | |
| 36 | [anonymized] | 2019-04-18 13:25 | 1.0.0 | logistic_reg logistic-regression umz-2019-challenge | 1.0000 | 1.0000 | |
| 258 | [anonymized] | 2019-04-18 13:09 | 1.0.0 | pieczarki logistic-regression | 0.9268 | 0.9158 | |
| 196 | [anonymized] | 2019-04-18 13:04 | 1.0.0 | my mushroom solution logistic-regression | 0.9722 | 0.9654 | |
| 35 | [anonymized] | 2019-04-17 05:37 | 1.0.0 | k nearest neighbors solution knn | 1.0000 | 1.0000 | |
| 34 | [anonymized] | 2019-04-16 20:45 | 1.0.0 | logistic reg solution logistic-regression umz-2019-challenge | 1.0000 | 1.0000 | |
| 287 | [anonymized] | 2019-04-16 15:23 | 1.0.0 | Naive bayes naive-bayes | 0.9066 | 0.9100 | |
| 229 | [anonymized] | 2019-04-16 15:17 | 1.0.0 | Logistic regression logistic-regression | 0.9432 | 0.9481 | |
| 206 | [anonymized] | 2019-04-16 14:53 | 1.0.0 | rozwiazanie2 logistic-regression | 0.9419 | 0.9493 | |
| 286 | [anonymized] | 2019-04-16 14:52 | 1.0.0 | mushroom bayes naive-bayes | 0.9066 | 0.9100 | |
| 285 | [anonymized] | 2019-04-16 14:47 | 1.0.0 | mushroom naive Bayes solution naive-bayes | 0.9066 | 0.9100 | |
| 284 | [anonymized] | 2019-04-16 14:47 | 1.0.0 | my solution naive-bayes umz-2019-challenge | 0.9066 | 0.9100 | |
| 228 | [anonymized] | 2019-04-16 14:12 | 1.0.0 | mushroom logistic-regression | 0.9432 | 0.9481 | |
| 227 | [anonymized] | 2019-04-16 14:05 | 1.0.0 | my mushrooms solution logistic-regression | 0.9432 | 0.9481 | |
| 226 | [anonymized] | 2019-04-16 14:03 | 1.0.0 | my solution logistic-regression umz-2019-challenge | 0.9432 | 0.9481 | |
| 33 | [anonymized] | 2019-04-16 13:18 | 1.0.0 | 16.04.2019. 15.17 logistic-regression umz-2019-challenge | 1.0000 | 1.0000 | |
| 180 | [anonymized] | 2019-04-16 12:57 | 1.0.0 | out.tsv logistic-regression | 0.9886 | 0.9839 | |
| 132 | [anonymized] | 2019-04-16 12:46 | 1.0.0 | regresja logistyczna 1 logistic-regression umz-2019-challenge | 1.0000 | 0.9988 | |
| 32 | [anonymized] | 2019-04-16 11:53 | 1.0.0 | knn commit uno knn umz-2019-challenge | 1.0000 | 1.0000 | |
| 225 | [anonymized] | 2019-04-16 10:54 | 1.0.0 | Cleanup2 logistic-regression | 0.9432 | 0.9481 | |
| 283 | [anonymized] | 2019-04-16 09:20 | 1.0.0 | Naive bayes solution naive-bayes | 0.9066 | 0.9100 | |
| 31 | [anonymized] | 2019-04-15 21:35 | 1.0.0 | Solution mushrooms KNN knn | 1.0000 | 1.0000 | |
| 150 | [anonymized] | 2019-04-15 21:27 | 1.0.0 | Solution mushrooms Naive-Bayes naive-bayes | 0.9924 | 0.9954 | |
| 205 | [anonymized] | 2019-04-15 21:26 | 1.0.0 | Solution mushrooms Naive-Bayes naive-bayes | 0.9407 | 0.9493 | |
| 236 | [anonymized] | 2019-04-15 19:31 | 1.0.0 | Logistic regression solution logistic-regression | 0.9432 | 0.9458 | |
| 30 | [anonymized] | 2019-04-15 16:21 | 1.0.0 | k nearest neighbors knn | 1.0000 | 1.0000 | |
| 29 | [anonymized] | 2019-04-15 15:59 | 1.0.0 | Solution mushrooms logistic logistic-regression | 1.0000 | 1.0000 | |
| 28 | [anonymized] | 2019-04-15 12:33 | 1.0.0 | KNN knn | 1.0000 | 1.0000 | |
| 27 | [anonymized] | 2019-04-15 11:45 | 1.0.0 | knn for n=3 knn | 1.0000 | 1.0000 | |
| 282 | [anonymized] | 2019-04-15 08:23 | 1.0.0 | naive bayes commit uno naive-bayes umz-2019-challenge | 0.9066 | 0.9100 | |
| 281 | [anonymized] | 2019-04-10 18:34 | 1.0.0 | Bayes second attempt naive-bayes umz-2019-challenge | 0.9066 | 0.9100 | |
| 224 | [anonymized] | 2019-04-10 18:23 | 1.0.0 | Logistic regression logistic-regression umz-2019-challenge | 0.9432 | 0.9481 | |
| 253 | Artur Nowakowski | 2019-04-09 15:55 | 1.0.0 | naive bayes naive-bayes | 0.9217 | 0.9181 | |
| 280 | [anonymized] | 2019-04-09 15:55 | 1.0.0 | naive bayes naive-bayes | 0.9066 | 0.9100 | |
| 223 | [anonymized] | 2019-04-09 15:34 | 1.0.0 | Commit grzyby logistic-regression | 0.9369 | 0.9481 | |
| 222 | [anonymized] | 2019-04-09 09:52 | 1.0.0 | finished logistic regression with sklearn logistic-regression | 0.9432 | 0.9481 | |
| 221 | [anonymized] | 2019-04-08 13:02 | 1.0.0 | logistic regression commit uno logistic-regression umz-2019-challenge | 0.9432 | 0.9481 | |
| 220 | [anonymized] | 2019-04-07 17:54 | 1.0.0 | i donot like mushrooms logistic-regression logistic-regression | 0.9444 | 0.9481 | |
| 219 | [anonymized] | 2019-04-07 17:48 | 1.0.0 | mushroomhead solution logistic-regression | 0.9444 | 0.9481 | |
| 179 | [anonymized] | 2019-04-04 20:18 | 1.0.0 | naive bayes one column naive-bayes | 0.9886 | 0.9839 | |
| 247 | [anonymized] | 2019-04-04 20:14 | 1.0.0 | naive bayes naive-bayes | 0.9457 | 0.9331 | |
| 279 | [anonymized] | 2019-04-04 13:24 | 1.0.0 | Naive Bayes naive-bayes | 0.9066 | 0.9100 | |
| 218 | [anonymized] | 2019-04-02 16:25 | 1.0.0 | Logistic regression mushrooms logistic-regression | 0.9432 | 0.9481 | |
| 217 | [anonymized] | 2019-04-01 19:11 | 1.0.0 | lr rm logistic-regression | 0.9432 | 0.9481 | |
| 199 | Artur Nowakowski | 2019-03-26 16:51 | 1.0.0 | Logistic regression ready made logistic-regression | 0.9571 | 0.9596 | |
| 216 | [anonymized] | 2019-03-26 16:43 | 1.0.0 | logistic regression ready-made logistic-regression | 0.9432 | 0.9481 | |
| 241 | Artur Nowakowski | 2019-03-26 16:02 | 1.0.0 | Logistic regression ready made | 0.9444 | 0.9446 | |
| 26 | [anonymized] | 2019-03-24 14:37 | 1.0.0 | Logistic regression logistic-regression | 1.0000 | 1.0000 | |
| 240 | [anonymized] | 2019-03-24 02:57 | 1.0.0 | first attempt, logistic regression logistic-regression | 0.9407 | 0.9446 | |
| 278 | [anonymized] | 2018-02-23 23:55 | 1.0.0 | naive bayes solution naive-bayes | 0.9066 | 0.9100 | |
| 25 | [anonymized] | 2018-02-23 23:43 | 1.0.0 | knn knn | 1.0000 | 1.0000 | |
| 154 | [anonymized] | 2018-02-15 23:17 | 1.0.0 | neural-network neural-network | 0.9747 | 0.9931 | |
| 153 | [anonymized] | 2018-02-15 23:07 | 1.0.0 | neural-network | 1.0000 | 0.9931 | |
| 186 | [anonymized] | 2018-02-15 22:54 | 1.0.0 | neural-network | 1.0000 | 0.9804 | |
| 234 | [anonymized] | 2018-02-15 22:52 | 1.0.0 | neural-network | 1.0000 | 0.9469 | |
| 322 | [anonymized] | 2018-02-15 22:44 | 1.0.0 | neural-network | 1.0000 | 0.5652 | |
| 323 | [anonymized] | 2018-02-15 21:19 | 1.0.0 | neural-network | 1.0000 | 0.5594 | |
| 331 | [anonymized] | 2018-02-15 21:08 | 1.0.0 | neural-network | 1.0000 | 0.4960 | |
| 336 | [anonymized] | 2018-02-15 20:46 | 1.0.0 | neural-network | 1.0000 | 0.4902 | |
| 24 | [anonymized] | 2018-02-13 23:39 | 1.0.0 | knn k=3 knn | 1.0000 | 1.0000 | |
| 182 | [anonymized] | 2018-02-13 23:36 | 1.0.0 | knn k=1 | 1.0000 | 0.9827 | |
| 181 | [anonymized] | 2018-02-13 23:31 | 1.0.0 | naive-bayes naive-bayes python | 0.9861 | 0.9827 | |
| 131 | [anonymized] | 2018-02-13 23:26 | 1.0.0 | logistic-regression python logistic-regression | 1.0000 | 0.9988 | |
| 318 | [anonymized] | 2018-02-13 23:25 | 1.0.0 | logistic-regression | 1.0000 | 0.6436 | |
| 317 | [anonymized] | 2018-02-13 22:24 | 1.0.0 | logistic-regression | 0.6616 | 0.6436 | |
| 17 | [anonymized] | 2018-01-29 15:14 | 1.0.0 | bestacc-lowestk knn | N/A | 1.0000 | |
| 16 | [anonymized] | 2018-01-29 15:12 | 1.0.0 | bestacc-lowestk | N/A | 1.0000 | |
| 22 | [anonymized] | 2018-01-29 14:59 | 1.0.0 | bestacc-lowestk | 0.9975 | 1.0000 | |
| 357 | [anonymized] | 2018-01-29 14:57 | 1.0.0 | bestacc-lowestk | N/A | N/A | |
| 130 | [anonymized] | 2018-01-29 14:46 | 1.0.0 | bestacc-lowestk | N/A | 0.9988 | |
| 129 | [anonymized] | 2018-01-29 14:35 | 1.0.0 | bestacc-lowestk | N/A | 0.9988 | |
| 214 | [anonymized] | 2018-01-28 22:47 | 1.0.0 | lr1 python logistic-regression | 0.9432 | 0.9481 | |
| 20 | [anonymized] | 2018-01-28 22:26 | 1.0.0 | nn2 python neural-network | 1.0000 | 1.0000 | |
| 345 | [anonymized] | 2018-01-28 22:15 | 1.0.0 | nn1 | 0.0000 | 0.0000 | |
| 277 | [anonymized] | 2018-01-28 20:50 | 1.0.0 | nb3 naive-bayes python | 0.9066 | 0.9100 | |
| 21 | [anonymized] | 2018-01-28 20:44 | 1.0.0 | knn1 python knn | 1.0000 | 1.0000 | |
| 344 | [anonymized] | 2018-01-28 20:37 | 1.0.0 | nb2 naive-bayes python | 0.9066 | 0.0000 | |
| 19 | kaczla | 2018-01-28 17:58 | 1.0.0 | Simple neural network neural-network | 1.0000 | 1.0000 | |
| 23 | [anonymized] | 2018-01-27 15:53 | 1.0.0 | mushroom neural network 2 neural-network | 1.0000 | 1.0000 | |
| 128 | [anonymized] | 2018-01-27 15:40 | 1.0.0 | mushroom neural network neural-network | 1.0000 | 0.9988 | |
| 314 | [anonymized] | 2018-01-27 13:52 | 1.0.0 | mushroom knn knn | 0.6869 | 0.7301 | |
| 316 | [anonymized] | 2018-01-27 13:44 | 1.0.0 | mushroom naive bayes naive-bayes | 0.6616 | 0.6840 | |
| 335 | [anonymized] | 2018-01-27 13:02 | 1.0.0 | mushroom logistic regression logistic-regression | 0.6705 | 0.4913 | |
| 15 | [anonymized] | 2018-01-27 03:02 | 1.0.0 | UMZ-2017-10 neural-network | 1.0000 | 1.0000 | |
| 257 | [anonymized] | 2018-01-27 02:39 | 1.0.0 | UMZ-2017-07 naive-bayes | 0.9230 | 0.9158 | |
| 144 | [anonymized] | 2018-01-27 02:35 | 1.0.0 | UMZ-2017-08 knn | 0.9975 | 0.9977 | |
| 215 | [anonymized] | 2018-01-27 02:26 | 1.0.0 | UMZ-2017-06 logistic-regression | 0.9369 | 0.9481 | |
| 355 | [anonymized] | 2018-01-27 02:25 | 1.0.0 | UMZ-2017-06 | N/A | N/A | |
| 356 | [anonymized] | 2018-01-27 01:51 | 1.0.0 | UMZ-2017-06 | 0.9369 | N/A | |
| 354 | [anonymized] | 2018-01-27 01:37 | 1.0.0 | UMZ-2017-06 | 0.9369 | N/A | |
| 353 | [anonymized] | 2018-01-27 01:21 | 1.0.0 | UMZ-2017-06 | 0.9369 | N/A | |
| 352 | [anonymized] | 2018-01-27 01:19 | 1.0.0 | UMZ-2017-06 | 0.9369 | N/A | |
| 351 | [anonymized] | 2018-01-27 01:15 | 1.0.0 | UMZ-2017-06 | 0.9369 | N/A | |
| 18 | [anonymized] | 2018-01-24 19:56 | 1.0.0 | neural-network neural-network | 1.0000 | 1.0000 | |
| 127 | [anonymized] | 2018-01-23 23:35 | 1.0.0 | siec neuronowa neural-network | 1.0000 | 0.9988 | |
| 13 | [anonymized] | 2018-01-22 10:34 | 1.0.0 | Neural network for classification v8 neural-network | 1.0000 | 1.0000 | |
| 202 | [anonymized] | 2018-01-21 17:17 | 1.0.0 | Neural network for classification v7 | 0.9571 | 0.9516 | |
| 201 | [anonymized] | 2018-01-21 17:12 | 1.0.0 | Neural network for classification v6 | 0.9583 | 0.9516 | |
| 249 | [anonymized] | 2018-01-21 16:59 | 1.0.0 | Neural network for classification v5 | 0.9306 | 0.9308 | |
| 250 | [anonymized] | 2018-01-21 16:42 | 1.0.0 | Neural network for classification v4 | 0.9343 | 0.9296 | |
| 248 | [anonymized] | 2018-01-21 16:39 | 1.0.0 | Neural network for classification v3 | 0.9369 | 0.9319 | |
| 252 | [anonymized] | 2018-01-21 16:22 | 1.0.0 | Neural network for classification v2 | 0.9268 | 0.9204 | |
| 313 | [anonymized] | 2018-01-21 15:09 | 1.0.0 | Neural network for classification v1 | 0.7159 | 0.7301 | |
| 276 | [anonymized] | 2018-01-20 16:21 | 1.0.0 | mushroms - bayes naive-bayes | 0.9066 | 0.9100 | |
| 14 | [anonymized] | 2018-01-20 15:41 | 1.0.0 | mushroms - knn knn | 1.0000 | 1.0000 | |
| 204 | [anonymized] | 2018-01-20 15:27 | 1.0.0 | mushroms - RegLog logistic-regression | 0.9419 | 0.9493 | |
| 213 | [anonymized] | 2018-01-20 14:54 | 1.0.0 | Regresja logistyczna - mushroms1 logistic-regression | 0.9369 | 0.9481 | |
| 243 | [anonymized] | 2018-01-19 21:10 | 1.0.0 | Zadanie 009 z kodem programu v1.0 ready-made logistic-regression 2-dimensional | 0.9470 | 0.9423 | |
| 12 | [anonymized] | 2018-01-15 16:50 | 1.0.0 | knn mushrooms knn | 1.0000 | 1.0000 | |
| 273 | [anonymized] | 2018-01-15 16:24 | 1.0.0 | bayeas naive-bayes | 0.9066 | 0.9100 | |
| 274 | [anonymized] | 2018-01-15 16:20 | 1.0.0 | reglog with code logistic-regression | 0.9066 | 0.9100 | |
| 272 | [anonymized] | 2018-01-14 18:56 | 1.0.0 | bayeas naive-bayes | 0.9066 | 0.9100 | |
| 239 | [anonymized] | 2018-01-14 18:38 | 1.0.0 | hello linear-regression | 0.9407 | 0.9446 | |
| 275 | [anonymized] | 2018-01-14 13:29 | 1.0.0 | Bayes naive-bayes | N/A | 0.9100 | |
| 11 | [anonymized] | 2018-01-14 13:28 | 1.0.0 | KNN knn | N/A | 1.0000 | |
| 10 | [anonymized] | 2018-01-14 13:17 | 1.0.0 | Neural network neural-network | N/A | 1.0000 | |
| 125 | [anonymized] | 2018-01-14 13:15 | 1.0.0 | Neural network | N/A | 0.9988 | |
| 185 | [anonymized] | 2018-01-14 13:06 | 1.0.0 | Back to approach with int mapping | N/A | 0.9804 | |
| 326 | [anonymized] | 2018-01-14 12:54 | 1.0.0 | one hot vectors with inner join | N/A | 0.5283 | |
| 337 | [anonymized] | 2018-01-14 12:52 | 1.0.0 | One hot vectors from pandas | N/A | 0.4879 | |
| 167 | [anonymized] | 2018-01-14 12:41 | 1.0.0 | naiwny bayes naive-bayes | 0.9811 | 0.9885 | |
| 325 | [anonymized] | 2018-01-14 12:38 | 1.0.0 | Neural network with changed input set | N/A | 0.5375 | |
| 124 | [anonymized] | 2018-01-14 12:35 | 1.0.0 | k najblizszych sasiadow knn | 1.0000 | 0.9988 | |
| 321 | [anonymized] | 2018-01-14 12:35 | 1.0.0 | Neural network with one hot vectors as input layer | N/A | 0.5663 | |
| 320 | [anonymized] | 2018-01-14 12:33 | 1.0.0 | Neural network with one hot vectors as input layer | N/A | 0.5698 | |
| 126 | [anonymized] | 2018-01-14 12:15 | 1.0.0 | regresja logistyczna na dowolnej liczbie cech ready-made logistic-regression multidimensional | 1.0000 | 0.9988 | |
| 152 | [anonymized] | 2018-01-14 10:51 | 1.0.0 | regresja logistyczna na 2 cechach ready-made logistic-regression 2-dimensional | 0.9962 | 0.9931 | |
| 200 | [anonymized] | 2018-01-13 20:58 | 1.0.0 | Neural Network Classification neural-network | 0.9710 | 0.9539 | |
| 350 | [anonymized] | 2018-01-13 20:44 | 1.0.0 | Neural Network Classification | N/A | N/A | |
| 8 | [anonymized] | 2018-01-13 18:52 | 1.0.0 | neural with more neurons neural-network | N/A | 1.0000 | |
| 174 | [anonymized] | 2018-01-13 18:41 | 1.0.0 | neural network neural-network | N/A | 0.9862 | |
| 143 | [anonymized] | 2018-01-08 16:38 | 1.0.0 | knn knn | N/A | 0.9977 | |
| 256 | [anonymized] | 2018-01-08 16:30 | 1.0.0 | nb naive-bayes | N/A | 0.9158 | |
| 255 | [anonymized] | 2018-01-08 16:30 | 1.0.0 | nb | N/A | 0.9158 | |
| 212 | [anonymized] | 2018-01-08 16:14 | 1.0.0 | sol1 logistic-regression | N/A | 0.9481 | |
| 147 | [anonymized] | 2018-01-07 22:00 | 1.0.0 | KNN v6 | 0.9975 | 0.9965 | |
| 149 | [anonymized] | 2018-01-07 21:52 | 1.0.0 | KNN v5 first 13 | 0.9975 | 0.9954 | |
| 146 | [anonymized] | 2018-01-07 21:47 | 1.0.0 | KNN v4 first 12 | 0.9975 | 0.9965 | |
| 158 | [anonymized] | 2018-01-07 21:43 | 1.0.0 | KNN v3 first half | 0.9962 | 0.9919 | |
| 193 | [anonymized] | 2018-01-07 21:38 | 1.0.0 | KNN v2 second half | 0.9609 | 0.9689 | |
| 6 | [anonymized] | 2018-01-07 21:17 | 1.0.0 | KNN v1 knn | 1.0000 | 1.0000 | |
| 271 | [anonymized] | 2018-01-07 21:11 | 1.0.0 | NaiveBayes v1 naive-bayes | 0.9066 | 0.9100 | |
| 238 | [anonymized] | 2018-01-07 21:00 | 1.0.0 | RegLog v1 logistic-regression | 0.9407 | 0.9446 | |
| 9 | [anonymized] | 2018-01-07 15:08 | 1.0.0 | knn knn | 1.0000 | 1.0000 | |
| 254 | [anonymized] | 2018-01-07 13:10 | 1.0.0 | logistic-regression mushrom logistic-regression | 0.9167 | 0.9170 | |
| 5 | kaczla | 2018-01-04 19:04 | 1.0.0 | K nearest neighbors, use MurmurHash3 for hashing knn | 1.0000 | 1.0000 | |
| 305 | kaczla | 2018-01-04 18:45 | 1.0.0 | Naive bayes, use MurmurHash3 for hashing naive-bayes | 0.8725 | 0.8962 | |
| 302 | kaczla | 2018-01-04 18:33 | 1.0.0 | Logistic regression, use MurmurHash3 for hashing logistic-regression | 0.9268 | 0.9066 | |
| 211 | [anonymized] | 2018-01-04 17:34 | 1.0.0 | Logic road to intoxication l2 logistic-regression | 0.9419 | 0.9481 | |
| 203 | [anonymized] | 2018-01-04 17:32 | 1.0.0 | Logical path to intoxication logistic-regression | 0.9419 | 0.9493 | |
| 7 | [anonymized] | 2018-01-04 17:29 | 1.0.0 | knn knn | 1.0000 | 1.0000 | |
| 4 | [anonymized] | 2018-01-04 17:13 | 1.0.0 | Avoid nearby toadstools! knn | 1.0000 | 1.0000 | |
| 270 | [anonymized] | 2018-01-04 16:50 | 1.0.0 | GaussianNB naive-bayes | 0.9066 | 0.9100 | |
| 269 | [anonymized] | 2018-01-03 21:35 | 1.0.0 | Bayes is not so naive naive-bayes | 0.9066 | 0.9100 | |
| 210 | [anonymized] | 2018-01-03 18:01 | 1.0.0 | logisticRegression, mushrooms logistic-regression | 0.9432 | 0.9481 | |
| 349 | [anonymized] | 2018-01-02 17:34 | 1.0.0 | my solution self-made knn | N/A | N/A | |
| 324 | [anonymized] | 2017-12-29 12:06 | 1.0.0 | nltk naivebayes naive-bayes | 0.5480 | 0.5409 | |
| 3 | [anonymized] | 2017-12-27 20:35 | 1.0.0 | Task 8. knn | 1.0000 | 1.0000 | |
| 268 | [anonymized] | 2017-12-27 20:00 | 1.0.0 | Task 7. naive-bayes | 0.9066 | 0.9100 | |
| 2 | [anonymized] | 2017-12-27 00:18 | 1.0.0 | Task 6. logistic-regression | 1.0000 | 1.0000 | |
| 209 | [anonymized] | 2017-12-20 16:26 | 1.0.0 | Edible shrooms logistic regression logistic-regression | 0.9369 | 0.9481 | |
| 315 | [anonymized] | 2017-12-18 17:03 | 1.0.0 | Naive Bayes fixed naive-bayes | 0.6616 | 0.6840 | |
| 334 | [anonymized] | 2017-12-18 16:57 | 1.0.0 | Logistic Regression fix logistic-regression | 0.6705 | 0.4913 | |
| 312 | [anonymized] | 2017-12-18 16:51 | 1.0.0 | KNN fixed knn | 0.6869 | 0.7301 | |
| 341 | [anonymized] | 2017-12-14 11:55 | 1.0.0 | KNN knn | 0.0972 | 0.0796 | |
| 343 | [anonymized] | 2017-12-14 11:30 | 1.0.0 | Naive Bayes naive-bayes | 0.0013 | 0.0000 | |
| 342 | [anonymized] | 2017-12-14 11:06 | 1.0.0 | Logical Regression logistic-regression | 0.0088 | 0.0127 | |
| 348 | [anonymized] | 2017-12-14 11:02 | 1.0.0 | Logistic Regression logistic-regression | N/A | N/A | |
| 208 | [anonymized] | 2017-12-11 17:33 | 1.0.0 | Fancy mapping to asci logistic-regression | N/A | 0.9481 | |
| 1 | [anonymized] | 2017-12-11 17:23 | 1.0.0 | K nearest neighbors python ready-made knn | 1.0000 | 1.0000 | |
| 267 | [anonymized] | 2017-12-11 17:22 | 1.0.0 | Naive-bayes naive-bayes | N/A | 0.9100 | |
| 207 | [anonymized] | 2017-12-11 17:18 | 1.0.0 | Logstic regression logistic-regression | N/A | 0.9481 | |
| 266 | [anonymized] | 2017-12-11 14:00 | 1.0.0 | Simple naive Bayes naive-bayes python ready-made | 0.9040 | 0.9100 | |
| 176 | [anonymized] | 2017-12-11 13:48 | 1.0.0 | Simple logistic regression python ready-made logistic-regression | 0.9861 | 0.9850 | |
| 329 | p/tlen | 2017-12-11 08:24 | 1.0.0 | all edible stupid | 0.5126 | 0.5098 | |
| 347 | p/tlen | 2017-12-11 08:21 | 1.0.0 | init stupid | N/A | N/A |