diff --git a/articlequality/feature_lists/nlwiki.py b/articlequality/feature_lists/nlwiki.py index b9b80a3..15a1f6d 100644 --- a/articlequality/feature_lists/nlwiki.py +++ b/articlequality/feature_lists/nlwiki.py @@ -16,7 +16,7 @@ name="nlwiki.revision.cn_templates") infobox_templates = wikitext.revision.template_names_matching( - r"infobox", name="nlwiki.revision.infobox_templates") + r"Infobox", name="nlwiki.revision.infobox_templates") # Links category_links = wikitext.revision.wikilink_titles_matching( @@ -54,6 +54,8 @@ max(wikitext.revision.content_chars, 1), cn_templates, cn_templates / max(wikitext.revision.content_chars, 1), + infobox_templates, + infobox_templates / max(wikitext.revision.content_chars, 1), ] wp10 = local_wiki + wikipedia.article diff --git a/datasets/nlwiki-20210520-score.stratified_sample_of_20.tsv b/datasets/nlwiki-20210520-score.stratified_sample_of_20.tsv new file mode 100644 index 0000000..26eb00a --- /dev/null +++ b/datasets/nlwiki-20210520-score.stratified_sample_of_20.tsv @@ -0,0 +1,100 @@ +3940056 Plagithmysus laticollis 54682230 20210520000000 E 1.1120835215246287 +3193908 Prosopistoma annamense 35610509 20210520000000 E 1.6687152698218732 +3668825 Purbasari (Karangjambu) 50692645 20210520000000 E 1.358488791040014 +2440435 Dysomma dolichosomatum 58307735 20210520000000 E 1.7901422684033623 +3672574 Pakunden (Ngluwar) 50687446 20210520000000 E 1.3650163039696572 +2423309 Sitticus kazakhstanicus 53592022 20210520000000 E 1.5644164810056125 +2797982 Thrushelton 56494083 20210520000000 E 1.6475139969315826 +3677229 Sendangmulyo (Sarang) 50695925 20210520000000 E 1.3650163039696572 +2478010 Melanotus staudingeri 57229539 20210520000000 E 1.4574945294740793 +2486204 Diabrotica gorhami 42744036 20210520000000 E 1.6817255597133278 +2486706 Paratriarius denotata 42725595 20210520000000 E 1.6756215280776126 +3930329 Glenea plagiventris 54653713 20210520000000 E 1.1032827930596403 +2635006 Pseudopanurgus dawsoni 36397648 20210520000000 E 1.370040265937408 +3207879 Gonionchus alastairi 55913476 20210520000000 E 1.514936801588155 +3631174 Malintang Julu 50681714 20210520000000 E 1.3974515158293983 +2686565 Hylaeus promontorii 36449223 20210520000000 E 1.3264765707281985 +3309102 Ischiolobos niveoscutum 38345967 20210520000000 E 1.7216891772880216 +2622762 Mimema tricolor 42940470 20210520000000 E 1.6700065559953956 +2689211 Lasioglossum perihirtum 36451541 20210520000000 E 1.3523342779951104 +2448899 Allocosa adolphifriederici 53071055 20210520000000 E 1.5352695800219385 +3390791 Commatica xanthocarpa 37510927 20210520000000 D 2.1685641929513757 +3248260 Plesiolembos ovalipes 37275513 20210520000000 D 2.0316478609617783 +1831863 Richard Michael Daley 58776420 20210520000000 D 2.0915110975207263 +2218808 Rhyacophila sierra 36190453 20210520000000 D 1.9507424963132995 +3363502 Glaucina spina 47241323 20210520000000 D 2.0807837658902466 +3521839 Adapantus osorioi 37412293 20210520000000 D 1.7359778791208813 +2909546 Apanteles characomae 33073517 20210520000000 D 1.923773604408404 +3598839 Neochrysocharis hirsutus 37695864 20210520000000 D 1.962673250403021 +3216709 Styela crinita 57569430 20210520000000 D 2.1543080987365513 +4378682 Jan Emmens 58741540 20210520000000 D 2.63273653832586 +3188352 Falcidens targotegulatus 35579774 20210520000000 D 1.9979864416525412 +3368541 Macrobathra platyzona 37508981 20210520000000 D 2.165148911198371 +4108850 Nicolas Leonard 58217197 20210520000000 D 2.1042441486601993 +3368001 Coleophora hackmanni 37508212 20210520000000 D 2.1685641929513757 +1103467 Blue Hotel 57781494 20210520000000 D 2.2343891791793786 +3525484 Athlithericles concordiae 37417282 20210520000000 D 1.8207967050537923 +4090137 Ochetobius 56445272 20210520000000 D 2.0078122386595014 +938149 Goot (wegenbouw) 39133263 20210520000000 D 2.1028785959533094 +218299 PROBA 58818193 20210520000000 D 2.6845846960425304 +461545 Pandan (Catanduanes) 49528148 20210520000000 D 2.2559253855058516 +5484683 Mariakapel ('t Rooth) 58425470 20210520000000 C 2.889760824652035 +3955169 Le Sauvage 47059599 20210520000000 C 2.9357181459094526 +776894 The Black Dahlia Murder 56542158 20210520000000 C 2.922124614049498 +592639 Sultanaat Langkat 57635613 20210520000000 C 2.9321486796206635 +3851701 Pebble 55942249 20210520000000 C 2.8748068740096566 +2140795 Carel Boshoff 56902810 20210520000000 C 2.9419846996773593 +838428 Epistasie 58892424 20210520000000 C 2.9478668214809276 +1032621 New Rochelle 58720234 20210520000000 C 2.912220246329882 +3770430 Lijst van voetbalinterlands Congo-Brazzaville - Tunesië 55152433 20210520000000 C 2.815007202266156 +4043258 Dyllan Lanser 55870561 20210520000000 C 2.7743905967204316 +4779766 Ondergrondse Vakschool 51369410 20210520000000 C 2.940578090154196 +5102230 Dennis Jastrzembski 56319942 20210520000000 C 2.775366073919686 +248545 Cosenza (provincie) 58663742 20210520000000 C 2.887675089913423 +2260583 Kasteel van La Ferté-Saint-Aubin 56827017 20210520000000 C 2.94328431400428 +3014677 Venloos Paeterke Triepel 53581248 20210520000000 C 2.970043393595098 +451316 GP La Marseillaise 58208700 20210520000000 C 2.9177026089784643 +4925563 Malderuscollege 57101094 20210520000000 C 2.822400453014968 +2869975 FC Elva 58678970 20210520000000 C 2.885733159301898 +4816002 Coöperatieve Visafslag Sint-Vincentius 58975857 20210520000000 C 2.921873839448678 +1276205 Albina Mayorova 53764353 20210520000000 C 2.7977177982222163 +62103 Boomleeuwerik 58899775 20210520000000 B 4.030970942784196 +3746142 Lijst van planetoïden 60101-60200 55416039 20210520000000 B 3.069136889435409 +235633 Echinaforce 57333350 20210520000000 B 3.4378091925730474 +150376 PCI Express 58766812 20210520000000 B 3.506363244544223 +21170 Memorial Van Damme 58464331 20210520000000 B 3.370506119241686 +761700 Jean-Jacques van Zuylen van Nyevelt 56350210 20210520000000 B 3.5659682590441553 +5127170 ATP Finals 2018 57176581 20210520000000 B 3.4043686924596095 +218932 Lijst van voetballers - K 58963971 20210520000000 B 3.8473820972980306 +4397482 Ontmenselijking 56645853 20210520000000 B 3.7684558194219853 +4246888 Grand Prix-wegrace van Joegoslavië 1977 57531404 20210520000000 B 3.830786581598484 +1609799 Prostitutie 58974505 20210520000000 B 4.2131456068638675 +84894 Overeenkomst (België) 55376724 20210520000000 B 3.7114202637060836 +644667 UEFA Cup 1973/74 56955742 20210520000000 B 3.035224637140471 +2822586 RSC Anderlecht in het seizoen 2012/13 58931755 20210520000000 B 4.166681590819936 +4774692 Jalta.nl 56564229 20210520000000 B 3.156967111150766 +2196089 Lijst van beschermd erfgoed in 's-Gravenbrakel 57179241 20210520000000 B 2.9408349813757058 +1836501 Arrest-Hamer 52073358 20210520000000 B 3.3079372184151796 +2128689 Equilibrio 58312142 20210520000000 B 3.626008775584598 +1764155 Jozef Rulof 57801691 20210520000000 B 3.937112435324917 +144815 Pierre Deligne 57131382 20210520000000 B 3.4622561271591548 +14064 Connexxion 58971366 20210520000000 A 4.627567074790931 +5053483 Katharina Kest 56135275 20210520000000 A 4.452917599028246 +1974 Zuid-Amerika 58957688 20210520000000 A 4.588428231876597 +5390953 Geschiedenis van escargot 58421048 20210520000000 A 4.368137051051847 +775243 Computerarchitectuur 55658248 20210520000000 A 4.334409828144067 +6552 Sterkte-zwakteanalyse 57726680 20210520000000 A 4.595406147795115 +1797907 Lijst van landen in 1950 58857393 20210520000000 A 4.5026175164277085 +5443206 Stationsstraat (Maastricht) 58962250 20210520000000 A 4.469567659812975 +21956 Geschiedenis van Israël 58982823 20210520000000 A 4.557405057169349 +1672749 Geschiedenis van de valuta van Tibet 51436939 20210520000000 A 4.429501147919208 +34090 Kat (dier) 58570032 20210520000000 A 4.65437283478286 +1659705 IOS (Apple) 58702315 20210520000000 A 4.356388100466542 +39180 Armeense Genocide 58890301 20210520000000 A 4.723102002589263 +2969522 Slag bij Spotsylvania Court House 56111790 20210520000000 A 4.664026626624674 +2837084 Falcon 9 58984397 20210520000000 A 4.5891881534373535 +31797 RandstadRail 58933334 20210520000000 A 4.613356190702632 +1138537 Joint Strike Fighter-programma 58476324 20210520000000 A 4.563734924654093 +5308015 Lijst van Britse ministers van Defensie 58977253 20210520000000 A 4.197250420656993 +1131204 Verdrag van Lissabon 58192908 20210520000000 A 4.632126589313782 +5069964 Vitesse in het seizoen 2018/19 58954177 20210520000000 A 4.325055825717039 diff --git a/model_info/nlwiki.wp10.md b/model_info/nlwiki.wp10.md index 945f6dd..f59d526 100644 --- a/model_info/nlwiki.wp10.md +++ b/model_info/nlwiki.wp10.md @@ -1,7 +1,7 @@ Model Information: - type: GradientBoosting - version: 0.8.0 - - params: {'tol': 0.0001, 'population_rates': None, 'random_state': None, 'n_estimators': 300, 'init': None, 'verbose': 0, 'min_impurity_split': None, 'center': True, 'labels': ['E', 'D', 'C', 'B', 'A'], 'min_samples_split': 2, 'criterion': 'friedman_mse', 'multilabel': False, 'scale': True, 'max_depth': 3, 'subsample': 1.0, 'ccp_alpha': 0.0, 'max_features': 'log2', 'validation_fraction': 0.1, 'learning_rate': 0.01, 'min_weight_fraction_leaf': 0.0, 'min_samples_leaf': 1, 'loss': 'deviance', 'warm_start': False, 'min_impurity_decrease': 0.0, 'n_iter_no_change': None, 'label_weights': None, 'max_leaf_nodes': None, 'presort': 'deprecated'} + - params: {'validation_fraction': 0.1, 'min_impurity_split': None, 'max_features': 'log2', 'scale': True, 'min_weight_fraction_leaf': 0.0, 'population_rates': None, 'multilabel': False, 'ccp_alpha': 0.0, 'criterion': 'friedman_mse', 'tol': 0.0001, 'random_state': None, 'labels': ['E', 'D', 'C', 'B', 'A'], 'max_leaf_nodes': None, 'min_samples_leaf': 1, 'max_depth': 3, 'label_weights': None, 'init': None, 'loss': 'deviance', 'learning_rate': 0.01, 'subsample': 1.0, 'warm_start': False, 'verbose': 0, 'min_impurity_decrease': 0.0, 'presort': 'deprecated', 'min_samples_split': 2, 'n_iter_no_change': None, 'n_estimators': 300, 'center': True} Environment: - revscoring_version: '2.8.2' - platform: 'Linux-4.19.0-0.bpo.14-amd64-x86_64-with-debian-9.4' @@ -21,11 +21,11 @@ Model Information: counts (n=1649): label n ~E ~D ~C ~B ~A ------- --- --- ---- ---- ---- ---- ---- - 'E' 330 --> 240 90 0 0 0 - 'D' 330 --> 2 268 44 16 0 - 'C' 330 --> 0 41 289 0 0 - 'B' 330 --> 0 23 3 205 99 - 'A' 329 --> 0 0 0 102 227 + 'E' 330 --> 222 108 0 0 0 + 'D' 330 --> 2 266 45 17 0 + 'C' 330 --> 0 40 290 0 0 + 'B' 330 --> 0 23 3 208 96 + 'A' 329 --> 0 0 0 98 231 rates: 'E' 'D' 'C' 'B' 'A' ---------- ----- ----- ----- ----- ----- @@ -34,51 +34,51 @@ Model Information: match_rate (micro=0.2, macro=0.2): E D C B A ----- ----- ----- ----- ----- - 0.147 0.256 0.204 0.196 0.198 + 0.136 0.265 0.205 0.196 0.199 filter_rate (micro=0.8, macro=0.8): E D C B A ----- ----- ----- ----- ----- - 0.853 0.744 0.796 0.804 0.802 - recall (micro=0.745, macro=0.745): - E D C B A - ----- ----- ----- ----- ---- - 0.727 0.812 0.876 0.621 0.69 - !recall (micro=0.936, macro=0.936): - E D C B A - ----- ----- ----- ----- ----- - 0.998 0.883 0.964 0.911 0.925 - precision (micro=0.764, macro=0.764): - E D C B A - ----- ----- ---- ----- ----- - 0.992 0.635 0.86 0.634 0.697 - !precision (micro=0.937, macro=0.937): + 0.864 0.735 0.795 0.804 0.801 + recall (micro=0.738, macro=0.738): + E D C B A + ----- ----- ----- ---- ----- + 0.673 0.806 0.879 0.63 0.702 + !recall (micro=0.935, macro=0.935): E D C B A ----- ---- ----- ----- ----- - 0.936 0.95 0.969 0.906 0.923 - f1 (micro=0.748, macro=0.748): - E D C B A - ----- ----- ----- ----- ----- - 0.839 0.713 0.868 0.628 0.693 - !f1 (micro=0.936, macro=0.936): + 0.998 0.87 0.964 0.913 0.927 + precision (micro=0.762, macro=0.762): E D C B A ----- ----- ----- ----- ----- - 0.966 0.915 0.967 0.908 0.924 - accuracy (micro=0.898, macro=0.898): - E D C B A - ----- ----- ----- ----- ----- - 0.944 0.869 0.947 0.853 0.878 - fpr (micro=0.064, macro=0.064): + 0.991 0.609 0.858 0.644 0.707 + !precision (micro=0.935, macro=0.935): + E D C B A + ----- ----- ---- ----- ----- + 0.924 0.947 0.97 0.908 0.926 + f1 (micro=0.741, macro=0.741): E D C B A ----- ----- ----- ----- ----- - 0.002 0.117 0.036 0.089 0.075 - roc_auc (micro=0.946, macro=0.946): + 0.801 0.693 0.868 0.637 0.705 + !f1 (micro=0.934, macro=0.934): + E D C B A + ---- ----- ----- ---- ----- + 0.96 0.907 0.967 0.91 0.926 + accuracy (micro=0.895, macro=0.895): E D C B A ----- ----- ----- ----- ----- - 0.989 0.921 0.979 0.902 0.939 - pr_auc (micro=0.804, macro=0.804): + 0.933 0.857 0.947 0.856 0.882 + fpr (micro=0.065, macro=0.065): + E D C B A + ----- ---- ----- ----- ----- + 0.002 0.13 0.036 0.087 0.073 + roc_auc (micro=0.945, macro=0.945): + E D C B A + ---- ----- ----- --- ----- + 0.99 0.919 0.979 0.9 0.938 + pr_auc (micro=0.802, macro=0.802): E D C B A ----- ----- ----- ----- ----- - 0.977 0.803 0.873 0.666 0.703 + 0.979 0.805 0.866 0.652 0.706 - - score_schema: {'title': 'Scikit learn-based classifier score with probability', 'properties': {'prediction': {'description': 'The most likely label predicted by the estimator', 'type': 'string'}, 'probability': {'description': 'A mapping of probabilities onto each of the potential output labels', 'properties': {'C': {'type': 'number'}, 'B': {'type': 'number'}, 'E': {'type': 'number'}, 'D': {'type': 'number'}, 'A': {'type': 'number'}}, 'type': 'object'}}, 'type': 'object'} + - score_schema: {'type': 'object', 'properties': {'probability': {'description': 'A mapping of probabilities onto each of the potential output labels', 'type': 'object', 'properties': {'A': {'type': 'number'}, 'D': {'type': 'number'}, 'B': {'type': 'number'}, 'C': {'type': 'number'}, 'E': {'type': 'number'}}}, 'prediction': {'description': 'The most likely label predicted by the estimator', 'type': 'string'}}, 'title': 'Scikit learn-based classifier score with probability'} diff --git a/models/nlwiki.wp10.gradient_boosting.model b/models/nlwiki.wp10.gradient_boosting.model index fae9945..0d45e9c 100644 --- a/models/nlwiki.wp10.gradient_boosting.model +++ b/models/nlwiki.wp10.gradient_boosting.model @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:bf62ce6eefadb2a081ff0115d88c6fc13f89817cc9c0ad87b15d4f81f0996955 -size 2364541 +oid sha256:b58796023e459c3dc9ac710907342cdd14b9fdd142bbf70b2fdfb1cfce077b8f +size 2369043 diff --git a/tuning_reports/nlwiki.wp10.md b/tuning_reports/nlwiki.wp10.md index 3702779..119fa32 100644 --- a/tuning_reports/nlwiki.wp10.md +++ b/tuning_reports/nlwiki.wp10.md @@ -1,7 +1,7 @@ # Model tuning report - Revscoring version: 2.8.2 - Features: articlequality.feature_lists.nlwiki.wp10 -- Date: 2021-03-11T16:55:47.377015 +- Date: 2021-04-14T16:25:04.789280 - Observations: 1649 - Labels: ["E", "D", "C", "B", "A"] - Statistic: accuracy.macro (maximize) @@ -10,174 +10,174 @@ # Top scoring configurations | model | accuracy.macro | params | |:-----------------------|-----------------:|:-------------------------------------------------------------------------------| -| RandomForestClassifier | 0.9144 | n_estimators=40, min_samples_leaf=13, criterion="entropy", max_features="log2" | -| RandomForestClassifier | 0.9112 | n_estimators=80, min_samples_leaf=13, criterion="entropy", max_features="log2" | -| GradientBoosting | 0.9107 | n_estimators=500, learning_rate=0.01, max_depth=1, max_features="log2" | -| RandomForestClassifier | 0.9066 | n_estimators=10, min_samples_leaf=13, criterion="entropy", max_features="log2" | -| GradientBoosting | 0.9064 | n_estimators=300, learning_rate=0.01, max_depth=1, max_features="log2" | -| GradientBoosting | 0.9047 | n_estimators=300, learning_rate=0.01, max_depth=3, max_features="log2" | -| RandomForestClassifier | 0.9025 | n_estimators=320, min_samples_leaf=3, criterion="entropy", max_features="log2" | -| RandomForestClassifier | 0.9025 | n_estimators=80, min_samples_leaf=3, criterion="entropy", max_features="log2" | -| RandomForestClassifier | 0.9022 | n_estimators=320, min_samples_leaf=1, criterion="entropy", max_features="log2" | -| RandomForestClassifier | 0.9022 | n_estimators=320, min_samples_leaf=3, criterion="gini", max_features="log2" | +| RandomForestClassifier | 0.9153 | n_estimators=40, min_samples_leaf=5, criterion="entropy", max_features="log2" | +| RandomForestClassifier | 0.9153 | n_estimators=20, min_samples_leaf=5, criterion="entropy", max_features="log2" | +| RandomForestClassifier | 0.9129 | n_estimators=10, min_samples_leaf=5, criterion="gini", max_features="log2" | +| RandomForestClassifier | 0.911 | n_estimators=20, min_samples_leaf=13, criterion="entropy", max_features="log2" | +| RandomForestClassifier | 0.9107 | n_estimators=10, min_samples_leaf=7, criterion="gini", max_features="log2" | +| GradientBoosting | 0.909 | n_estimators=500, max_depth=1, learning_rate=0.01, max_features="log2" | +| GradientBoosting | 0.9085 | n_estimators=700, max_depth=1, learning_rate=0.01, max_features="log2" | +| GradientBoosting | 0.9066 | n_estimators=300, max_depth=3, learning_rate=0.01, max_features="log2" | +| GradientBoosting | 0.9052 | n_estimators=300, max_depth=1, learning_rate=0.01, max_features="log2" | +| RandomForestClassifier | 0.9047 | n_estimators=160, min_samples_leaf=5, criterion="entropy", max_features="log2" | # Models -## BernoulliNB -| accuracy.macro | params | -|-----------------:|:---------| -| 0.8904 | | +## LogisticRegression +| accuracy.macro | params | +|-----------------:|:--------------------| +| 0.878 | C=0.1, penalty="l2" | +| 0.8739 | C=1, penalty="l2" | +| 0.8734 | C=10, penalty="l2" | -## GaussianNB +## BernoulliNB | accuracy.macro | params | |-----------------:|:---------| -| 0.845 | | - -## GradientBoosting -| accuracy.macro | params | -|-----------------:|:-----------------------------------------------------------------------| -| 0.9107 | n_estimators=500, learning_rate=0.01, max_depth=1, max_features="log2" | -| 0.9064 | n_estimators=300, learning_rate=0.01, max_depth=1, max_features="log2" | -| 0.9047 | n_estimators=300, learning_rate=0.01, max_depth=3, max_features="log2" | -| 0.9018 | n_estimators=100, learning_rate=0.01, max_depth=1, max_features="log2" | -| 0.8996 | n_estimators=700, learning_rate=0.1, max_depth=7, max_features="log2" | -| 0.8988 | n_estimators=100, learning_rate=0.5, max_depth=7, max_features="log2" | -| 0.8959 | n_estimators=100, learning_rate=0.01, max_depth=3, max_features="log2" | -| 0.8947 | n_estimators=500, learning_rate=0.1, max_depth=7, max_features="log2" | -| 0.8908 | n_estimators=700, learning_rate=0.01, max_depth=1, max_features="log2" | -| 0.8908 | n_estimators=100, learning_rate=0.01, max_depth=7, max_features="log2" | -| 0.8908 | n_estimators=100, learning_rate=0.1, max_depth=1, max_features="log2" | -| 0.8908 | n_estimators=500, learning_rate=0.01, max_depth=3, max_features="log2" | -| 0.8896 | n_estimators=300, learning_rate=0.5, max_depth=7, max_features="log2" | -| 0.8896 | n_estimators=300, learning_rate=0.1, max_depth=7, max_features="log2" | -| 0.8891 | n_estimators=700, learning_rate=0.01, max_depth=3, max_features="log2" | -| 0.8889 | n_estimators=700, learning_rate=0.01, max_depth=7, max_features="log2" | -| 0.8882 | n_estimators=500, learning_rate=0.1, max_depth=1, max_features="log2" | -| 0.8877 | n_estimators=500, learning_rate=0.01, max_depth=5, max_features="log2" | -| 0.8874 | n_estimators=500, learning_rate=0.5, max_depth=3, max_features="log2" | -| 0.8867 | n_estimators=300, learning_rate=0.1, max_depth=1, max_features="log2" | -| 0.8843 | n_estimators=700, learning_rate=0.1, max_depth=1, max_features="log2" | -| 0.8833 | n_estimators=300, learning_rate=0.01, max_depth=7, max_features="log2" | -| 0.8824 | n_estimators=100, learning_rate=0.1, max_depth=3, max_features="log2" | -| 0.8809 | n_estimators=300, learning_rate=0.1, max_depth=5, max_features="log2" | -| 0.8809 | n_estimators=500, learning_rate=0.5, max_depth=7, max_features="log2" | -| 0.8804 | n_estimators=100, learning_rate=0.01, max_depth=5, max_features="log2" | -| 0.8802 | n_estimators=300, learning_rate=0.5, max_depth=1, max_features="log2" | -| 0.8799 | n_estimators=100, learning_rate=0.1, max_depth=5, max_features="log2" | -| 0.8787 | n_estimators=100, learning_rate=0.5, max_depth=5, max_features="log2" | -| 0.8782 | n_estimators=300, learning_rate=0.01, max_depth=5, max_features="log2" | -| 0.878 | n_estimators=100, learning_rate=0.5, max_depth=1, max_features="log2" | -| 0.8777 | n_estimators=100, learning_rate=0.1, max_depth=7, max_features="log2" | -| 0.877 | n_estimators=700, learning_rate=0.1, max_depth=5, max_features="log2" | -| 0.876 | n_estimators=700, learning_rate=0.01, max_depth=5, max_features="log2" | -| 0.8758 | n_estimators=300, learning_rate=0.1, max_depth=3, max_features="log2" | -| 0.8756 | n_estimators=700, learning_rate=0.5, max_depth=7, max_features="log2" | -| 0.8756 | n_estimators=100, learning_rate=0.5, max_depth=3, max_features="log2" | -| 0.8741 | n_estimators=300, learning_rate=1, max_depth=3, max_features="log2" | -| 0.8727 | n_estimators=500, learning_rate=0.1, max_depth=3, max_features="log2" | -| 0.8727 | n_estimators=700, learning_rate=0.5, max_depth=1, max_features="log2" | -| 0.8724 | n_estimators=500, learning_rate=0.5, max_depth=1, max_features="log2" | -| 0.8714 | n_estimators=300, learning_rate=0.5, max_depth=3, max_features="log2" | -| 0.8707 | n_estimators=500, learning_rate=0.1, max_depth=5, max_features="log2" | -| 0.87 | n_estimators=100, learning_rate=1, max_depth=1, max_features="log2" | -| 0.869 | n_estimators=700, learning_rate=0.5, max_depth=3, max_features="log2" | -| 0.8685 | n_estimators=700, learning_rate=0.5, max_depth=5, max_features="log2" | -| 0.868 | n_estimators=700, learning_rate=0.1, max_depth=3, max_features="log2" | -| 0.8654 | n_estimators=300, learning_rate=0.5, max_depth=5, max_features="log2" | -| 0.8642 | n_estimators=500, learning_rate=0.01, max_depth=7, max_features="log2" | -| 0.861 | n_estimators=500, learning_rate=0.5, max_depth=5, max_features="log2" | -| 0.8588 | n_estimators=300, learning_rate=1, max_depth=7, max_features="log2" | -| 0.852 | n_estimators=100, learning_rate=1, max_depth=7, max_features="log2" | -| 0.8513 | n_estimators=500, learning_rate=1, max_depth=1, max_features="log2" | -| 0.8469 | n_estimators=700, learning_rate=1, max_depth=5, max_features="log2" | -| 0.8457 | n_estimators=100, learning_rate=1, max_depth=5, max_features="log2" | -| 0.8396 | n_estimators=300, learning_rate=1, max_depth=1, max_features="log2" | -| 0.8351 | n_estimators=500, learning_rate=1, max_depth=5, max_features="log2" | -| 0.8348 | n_estimators=300, learning_rate=1, max_depth=5, max_features="log2" | -| 0.8273 | n_estimators=500, learning_rate=1, max_depth=3, max_features="log2" | -| 0.8166 | n_estimators=700, learning_rate=1, max_depth=1, max_features="log2" | -| 0.8101 | n_estimators=100, learning_rate=1, max_depth=3, max_features="log2" | -| 0.7938 | n_estimators=700, learning_rate=1, max_depth=7, max_features="log2" | -| 0.7732 | n_estimators=500, learning_rate=1, max_depth=7, max_features="log2" | -| 0.7601 | n_estimators=700, learning_rate=1, max_depth=3, max_features="log2" | +| 0.8918 | | ## RandomForestClassifier | accuracy.macro | params | |-----------------:|:--------------------------------------------------------------------------------| -| 0.9144 | n_estimators=40, min_samples_leaf=13, criterion="entropy", max_features="log2" | -| 0.9112 | n_estimators=80, min_samples_leaf=13, criterion="entropy", max_features="log2" | -| 0.9066 | n_estimators=10, min_samples_leaf=13, criterion="entropy", max_features="log2" | -| 0.9025 | n_estimators=320, min_samples_leaf=3, criterion="entropy", max_features="log2" | -| 0.9025 | n_estimators=80, min_samples_leaf=3, criterion="entropy", max_features="log2" | -| 0.9022 | n_estimators=320, min_samples_leaf=1, criterion="entropy", max_features="log2" | -| 0.9022 | n_estimators=320, min_samples_leaf=3, criterion="gini", max_features="log2" | -| 0.902 | n_estimators=160, min_samples_leaf=3, criterion="gini", max_features="log2" | -| 0.9018 | n_estimators=640, min_samples_leaf=7, criterion="gini", max_features="log2" | -| 0.9018 | n_estimators=640, min_samples_leaf=5, criterion="entropy", max_features="log2" | -| 0.9018 | n_estimators=80, min_samples_leaf=5, criterion="gini", max_features="log2" | -| 0.9015 | n_estimators=160, min_samples_leaf=3, criterion="entropy", max_features="log2" | -| 0.9015 | n_estimators=640, min_samples_leaf=3, criterion="gini", max_features="log2" | -| 0.9013 | n_estimators=640, min_samples_leaf=3, criterion="entropy", max_features="log2" | -| 0.9013 | n_estimators=640, min_samples_leaf=5, criterion="gini", max_features="log2" | -| 0.9008 | n_estimators=160, min_samples_leaf=5, criterion="gini", max_features="log2" | -| 0.9005 | n_estimators=160, min_samples_leaf=7, criterion="entropy", max_features="log2" | -| 0.9005 | n_estimators=40, min_samples_leaf=5, criterion="gini", max_features="log2" | -| 0.9003 | n_estimators=640, min_samples_leaf=7, criterion="entropy", max_features="log2" | -| 0.9003 | n_estimators=80, min_samples_leaf=3, criterion="gini", max_features="log2" | -| 0.9003 | n_estimators=320, min_samples_leaf=5, criterion="gini", max_features="log2" | -| 0.9001 | n_estimators=640, min_samples_leaf=13, criterion="gini", max_features="log2" | -| 0.9001 | n_estimators=160, min_samples_leaf=7, criterion="gini", max_features="log2" | -| 0.9001 | n_estimators=20, min_samples_leaf=3, criterion="entropy", max_features="log2" | -| 0.9001 | n_estimators=320, min_samples_leaf=7, criterion="gini", max_features="log2" | -| 0.8996 | n_estimators=40, min_samples_leaf=7, criterion="gini", max_features="log2" | -| 0.8996 | n_estimators=320, min_samples_leaf=7, criterion="entropy", max_features="log2" | -| 0.8996 | n_estimators=40, min_samples_leaf=3, criterion="gini", max_features="log2" | -| 0.8993 | n_estimators=640, min_samples_leaf=13, criterion="entropy", max_features="log2" | -| 0.8993 | n_estimators=160, min_samples_leaf=5, criterion="entropy", max_features="log2" | -| 0.8993 | n_estimators=640, min_samples_leaf=1, criterion="entropy", max_features="log2" | -| 0.8993 | n_estimators=80, min_samples_leaf=7, criterion="entropy", max_features="log2" | -| 0.8991 | n_estimators=20, min_samples_leaf=1, criterion="gini", max_features="log2" | -| 0.8988 | n_estimators=320, min_samples_leaf=13, criterion="entropy", max_features="log2" | -| 0.8988 | n_estimators=80, min_samples_leaf=13, criterion="gini", max_features="log2" | -| 0.8988 | n_estimators=80, min_samples_leaf=5, criterion="entropy", max_features="log2" | -| 0.8988 | n_estimators=80, min_samples_leaf=7, criterion="gini", max_features="log2" | -| 0.8988 | n_estimators=40, min_samples_leaf=5, criterion="entropy", max_features="log2" | -| 0.8986 | n_estimators=320, min_samples_leaf=5, criterion="entropy", max_features="log2" | -| 0.8986 | n_estimators=10, min_samples_leaf=13, criterion="gini", max_features="log2" | -| 0.8984 | n_estimators=80, min_samples_leaf=1, criterion="entropy", max_features="log2" | -| 0.8981 | n_estimators=320, min_samples_leaf=13, criterion="gini", max_features="log2" | -| 0.8981 | n_estimators=20, min_samples_leaf=7, criterion="gini", max_features="log2" | -| 0.8979 | n_estimators=160, min_samples_leaf=13, criterion="gini", max_features="log2" | -| 0.8976 | n_estimators=10, min_samples_leaf=3, criterion="entropy", max_features="log2" | -| 0.8974 | n_estimators=20, min_samples_leaf=13, criterion="gini", max_features="log2" | -| 0.8974 | n_estimators=20, min_samples_leaf=5, criterion="entropy", max_features="log2" | +| 0.9153 | n_estimators=40, min_samples_leaf=5, criterion="entropy", max_features="log2" | +| 0.9153 | n_estimators=20, min_samples_leaf=5, criterion="entropy", max_features="log2" | +| 0.9129 | n_estimators=10, min_samples_leaf=5, criterion="gini", max_features="log2" | +| 0.911 | n_estimators=20, min_samples_leaf=13, criterion="entropy", max_features="log2" | +| 0.9107 | n_estimators=10, min_samples_leaf=7, criterion="gini", max_features="log2" | +| 0.9047 | n_estimators=160, min_samples_leaf=5, criterion="entropy", max_features="log2" | +| 0.9044 | n_estimators=160, min_samples_leaf=3, criterion="gini", max_features="log2" | +| 0.9044 | n_estimators=320, min_samples_leaf=5, criterion="gini", max_features="log2" | +| 0.9044 | n_estimators=640, min_samples_leaf=5, criterion="gini", max_features="log2" | +| 0.9035 | n_estimators=640, min_samples_leaf=3, criterion="gini", max_features="log2" | +| 0.9032 | n_estimators=80, min_samples_leaf=3, criterion="gini", max_features="log2" | +| 0.903 | n_estimators=20, min_samples_leaf=7, criterion="gini", max_features="log2" | +| 0.903 | n_estimators=160, min_samples_leaf=5, criterion="gini", max_features="log2" | +| 0.903 | n_estimators=640, min_samples_leaf=3, criterion="entropy", max_features="log2" | +| 0.903 | n_estimators=640, min_samples_leaf=1, criterion="entropy", max_features="log2" | +| 0.903 | n_estimators=320, min_samples_leaf=3, criterion="entropy", max_features="log2" | +| 0.9025 | n_estimators=160, min_samples_leaf=3, criterion="entropy", max_features="log2" | +| 0.9022 | n_estimators=80, min_samples_leaf=3, criterion="entropy", max_features="log2" | +| 0.9022 | n_estimators=640, min_samples_leaf=5, criterion="entropy", max_features="log2" | +| 0.9022 | n_estimators=320, min_samples_leaf=5, criterion="entropy", max_features="log2" | +| 0.9018 | n_estimators=80, min_samples_leaf=5, criterion="entropy", max_features="log2" | +| 0.9018 | n_estimators=80, min_samples_leaf=1, criterion="entropy", max_features="log2" | +| 0.9018 | n_estimators=160, min_samples_leaf=7, criterion="gini", max_features="log2" | +| 0.9015 | n_estimators=320, min_samples_leaf=7, criterion="entropy", max_features="log2" | +| 0.9015 | n_estimators=320, min_samples_leaf=3, criterion="gini", max_features="log2" | +| 0.9013 | n_estimators=320, min_samples_leaf=7, criterion="gini", max_features="log2" | +| 0.901 | n_estimators=80, min_samples_leaf=5, criterion="gini", max_features="log2" | +| 0.9008 | n_estimators=160, min_samples_leaf=7, criterion="entropy", max_features="log2" | +| 0.9008 | n_estimators=640, min_samples_leaf=7, criterion="gini", max_features="log2" | +| 0.9005 | n_estimators=640, min_samples_leaf=7, criterion="entropy", max_features="log2" | +| 0.9005 | n_estimators=40, min_samples_leaf=3, criterion="gini", max_features="log2" | +| 0.9003 | n_estimators=40, min_samples_leaf=7, criterion="gini", max_features="log2" | +| 0.9003 | n_estimators=10, min_samples_leaf=3, criterion="gini", max_features="log2" | +| 0.9003 | n_estimators=80, min_samples_leaf=7, criterion="gini", max_features="log2" | +| 0.9003 | n_estimators=40, min_samples_leaf=3, criterion="entropy", max_features="log2" | +| 0.9001 | n_estimators=160, min_samples_leaf=13, criterion="entropy", max_features="log2" | +| 0.8996 | n_estimators=320, min_samples_leaf=13, criterion="entropy", max_features="log2" | +| 0.8991 | n_estimators=80, min_samples_leaf=7, criterion="entropy", max_features="log2" | +| 0.8986 | n_estimators=80, min_samples_leaf=13, criterion="gini", max_features="log2" | +| 0.8986 | n_estimators=320, min_samples_leaf=13, criterion="gini", max_features="log2" | +| 0.8986 | n_estimators=20, min_samples_leaf=7, criterion="entropy", max_features="log2" | +| 0.8986 | n_estimators=40, min_samples_leaf=1, criterion="entropy", max_features="log2" | +| 0.8984 | n_estimators=640, min_samples_leaf=13, criterion="entropy", max_features="log2" | +| 0.8984 | n_estimators=40, min_samples_leaf=13, criterion="gini", max_features="log2" | +| 0.8981 | n_estimators=40, min_samples_leaf=7, criterion="entropy", max_features="log2" | +| 0.8979 | n_estimators=160, min_samples_leaf=1, criterion="entropy", max_features="log2" | +| 0.8976 | n_estimators=160, min_samples_leaf=13, criterion="gini", max_features="log2" | +| 0.8974 | n_estimators=640, min_samples_leaf=13, criterion="gini", max_features="log2" | | 0.8969 | n_estimators=10, min_samples_leaf=5, criterion="entropy", max_features="log2" | -| 0.8964 | n_estimators=10, min_samples_leaf=7, criterion="gini", max_features="log2" | -| 0.8962 | n_estimators=20, min_samples_leaf=5, criterion="gini", max_features="log2" | -| 0.8959 | n_estimators=20, min_samples_leaf=7, criterion="entropy", max_features="log2" | -| 0.8957 | n_estimators=40, min_samples_leaf=3, criterion="entropy", max_features="log2" | -| 0.8957 | n_estimators=40, min_samples_leaf=13, criterion="gini", max_features="log2" | -| 0.895 | n_estimators=160, min_samples_leaf=13, criterion="entropy", max_features="log2" | -| 0.8938 | n_estimators=80, min_samples_leaf=1, criterion="gini", max_features="log2" | -| 0.8933 | n_estimators=10, min_samples_leaf=5, criterion="gini", max_features="log2" | -| 0.893 | n_estimators=640, min_samples_leaf=1, criterion="gini", max_features="log2" | -| 0.8928 | n_estimators=10, min_samples_leaf=3, criterion="gini", max_features="log2" | -| 0.8925 | n_estimators=160, min_samples_leaf=1, criterion="gini", max_features="log2" | -| 0.8923 | n_estimators=320, min_samples_leaf=1, criterion="gini", max_features="log2" | -| 0.8921 | n_estimators=10, min_samples_leaf=1, criterion="entropy", max_features="log2" | -| 0.8906 | n_estimators=160, min_samples_leaf=1, criterion="entropy", max_features="log2" | -| 0.8874 | n_estimators=20, min_samples_leaf=3, criterion="gini", max_features="log2" | -| 0.8853 | n_estimators=40, min_samples_leaf=7, criterion="entropy", max_features="log2" | -| 0.8843 | n_estimators=20, min_samples_leaf=1, criterion="entropy", max_features="log2" | -| 0.8838 | n_estimators=40, min_samples_leaf=1, criterion="entropy", max_features="log2" | -| 0.8826 | n_estimators=10, min_samples_leaf=7, criterion="entropy", max_features="log2" | -| 0.8794 | n_estimators=20, min_samples_leaf=13, criterion="entropy", max_features="log2" | -| 0.8647 | n_estimators=40, min_samples_leaf=1, criterion="gini", max_features="log2" | -| 0.8634 | n_estimators=10, min_samples_leaf=1, criterion="gini", max_features="log2" | +| 0.8967 | n_estimators=80, min_samples_leaf=13, criterion="entropy", max_features="log2" | +| 0.8955 | n_estimators=40, min_samples_leaf=13, criterion="entropy", max_features="log2" | +| 0.895 | n_estimators=20, min_samples_leaf=3, criterion="gini", max_features="log2" | +| 0.8947 | n_estimators=320, min_samples_leaf=1, criterion="entropy", max_features="log2" | +| 0.8945 | n_estimators=640, min_samples_leaf=1, criterion="gini", max_features="log2" | +| 0.894 | n_estimators=20, min_samples_leaf=13, criterion="gini", max_features="log2" | +| 0.8937 | n_estimators=160, min_samples_leaf=1, criterion="gini", max_features="log2" | +| 0.8928 | n_estimators=20, min_samples_leaf=3, criterion="entropy", max_features="log2" | +| 0.8923 | n_estimators=40, min_samples_leaf=1, criterion="gini", max_features="log2" | +| 0.8921 | n_estimators=80, min_samples_leaf=1, criterion="gini", max_features="log2" | +| 0.8921 | n_estimators=320, min_samples_leaf=1, criterion="gini", max_features="log2" | +| 0.8916 | n_estimators=10, min_samples_leaf=13, criterion="gini", max_features="log2" | +| 0.8913 | n_estimators=10, min_samples_leaf=1, criterion="entropy", max_features="log2" | +| 0.8911 | n_estimators=20, min_samples_leaf=5, criterion="gini", max_features="log2" | +| 0.8906 | n_estimators=10, min_samples_leaf=13, criterion="entropy", max_features="log2" | +| 0.8889 | n_estimators=20, min_samples_leaf=1, criterion="entropy", max_features="log2" | +| 0.8862 | n_estimators=10, min_samples_leaf=7, criterion="entropy", max_features="log2" | +| 0.886 | n_estimators=40, min_samples_leaf=5, criterion="gini", max_features="log2" | +| 0.8826 | n_estimators=10, min_samples_leaf=3, criterion="entropy", max_features="log2" | +| 0.8734 | n_estimators=20, min_samples_leaf=1, criterion="gini", max_features="log2" | +| 0.8571 | n_estimators=10, min_samples_leaf=1, criterion="gini", max_features="log2" | -## LogisticRegression -| accuracy.macro | params | -|-----------------:|:--------------------| -| 0.8894 | C=1, penalty="l2" | -| 0.8872 | C=10, penalty="l2" | -| 0.8797 | C=0.1, penalty="l2" | +## GradientBoosting +| accuracy.macro | params | +|-----------------:|:-----------------------------------------------------------------------| +| 0.909 | n_estimators=500, max_depth=1, learning_rate=0.01, max_features="log2" | +| 0.9085 | n_estimators=700, max_depth=1, learning_rate=0.01, max_features="log2" | +| 0.9066 | n_estimators=300, max_depth=3, learning_rate=0.01, max_features="log2" | +| 0.9052 | n_estimators=300, max_depth=1, learning_rate=0.01, max_features="log2" | +| 0.9025 | n_estimators=700, max_depth=7, learning_rate=0.5, max_features="log2" | +| 0.902 | n_estimators=500, max_depth=5, learning_rate=0.1, max_features="log2" | +| 0.901 | n_estimators=100, max_depth=5, learning_rate=0.1, max_features="log2" | +| 0.896 | n_estimators=100, max_depth=1, learning_rate=0.01, max_features="log2" | +| 0.8959 | n_estimators=100, max_depth=7, learning_rate=0.5, max_features="log2" | +| 0.8942 | n_estimators=700, max_depth=7, learning_rate=0.1, max_features="log2" | +| 0.894 | n_estimators=700, max_depth=3, learning_rate=0.01, max_features="log2" | +| 0.8935 | n_estimators=100, max_depth=3, learning_rate=0.01, max_features="log2" | +| 0.8921 | n_estimators=500, max_depth=3, learning_rate=0.01, max_features="log2" | +| 0.8918 | n_estimators=100, max_depth=3, learning_rate=0.1, max_features="log2" | +| 0.8906 | n_estimators=500, max_depth=1, learning_rate=0.1, max_features="log2" | +| 0.8896 | n_estimators=300, max_depth=5, learning_rate=0.5, max_features="log2" | +| 0.8891 | n_estimators=500, max_depth=5, learning_rate=0.5, max_features="log2" | +| 0.8884 | n_estimators=100, max_depth=1, learning_rate=0.1, max_features="log2" | +| 0.8874 | n_estimators=500, max_depth=7, learning_rate=0.5, max_features="log2" | +| 0.887 | n_estimators=300, max_depth=5, learning_rate=0.1, max_features="log2" | +| 0.8867 | n_estimators=100, max_depth=1, learning_rate=0.5, max_features="log2" | +| 0.8862 | n_estimators=500, max_depth=3, learning_rate=0.5, max_features="log2" | +| 0.8862 | n_estimators=500, max_depth=7, learning_rate=0.1, max_features="log2" | +| 0.8862 | n_estimators=700, max_depth=1, learning_rate=0.1, max_features="log2" | +| 0.8838 | n_estimators=300, max_depth=7, learning_rate=0.5, max_features="log2" | +| 0.8838 | n_estimators=700, max_depth=7, learning_rate=0.01, max_features="log2" | +| 0.8833 | n_estimators=300, max_depth=1, learning_rate=0.1, max_features="log2" | +| 0.8828 | n_estimators=300, max_depth=1, learning_rate=0.5, max_features="log2" | +| 0.8819 | n_estimators=700, max_depth=5, learning_rate=0.01, max_features="log2" | +| 0.8819 | n_estimators=300, max_depth=1, learning_rate=1, max_features="log2" | +| 0.8816 | n_estimators=700, max_depth=5, learning_rate=0.1, max_features="log2" | +| 0.8816 | n_estimators=500, max_depth=5, learning_rate=0.01, max_features="log2" | +| 0.8816 | n_estimators=500, max_depth=1, learning_rate=0.5, max_features="log2" | +| 0.8807 | n_estimators=100, max_depth=7, learning_rate=0.01, max_features="log2" | +| 0.8802 | n_estimators=100, max_depth=5, learning_rate=0.01, max_features="log2" | +| 0.8802 | n_estimators=300, max_depth=7, learning_rate=0.1, max_features="log2" | +| 0.8792 | n_estimators=300, max_depth=3, learning_rate=0.1, max_features="log2" | +| 0.8792 | n_estimators=300, max_depth=7, learning_rate=0.01, max_features="log2" | +| 0.8785 | n_estimators=700, max_depth=1, learning_rate=0.5, max_features="log2" | +| 0.8782 | n_estimators=500, max_depth=7, learning_rate=0.01, max_features="log2" | +| 0.878 | n_estimators=300, max_depth=5, learning_rate=0.01, max_features="log2" | +| 0.8777 | n_estimators=100, max_depth=3, learning_rate=0.5, max_features="log2" | +| 0.8775 | n_estimators=100, max_depth=7, learning_rate=0.1, max_features="log2" | +| 0.877 | n_estimators=700, max_depth=5, learning_rate=0.5, max_features="log2" | +| 0.8756 | n_estimators=700, max_depth=3, learning_rate=0.1, max_features="log2" | +| 0.8739 | n_estimators=100, max_depth=5, learning_rate=0.5, max_features="log2" | +| 0.8731 | n_estimators=500, max_depth=3, learning_rate=0.1, max_features="log2" | +| 0.8675 | n_estimators=100, max_depth=1, learning_rate=1, max_features="log2" | +| 0.8659 | n_estimators=100, max_depth=5, learning_rate=1, max_features="log2" | +| 0.8656 | n_estimators=700, max_depth=3, learning_rate=1, max_features="log2" | +| 0.8608 | n_estimators=300, max_depth=3, learning_rate=0.5, max_features="log2" | +| 0.86 | n_estimators=700, max_depth=3, learning_rate=0.5, max_features="log2" | +| 0.8535 | n_estimators=300, max_depth=5, learning_rate=1, max_features="log2" | +| 0.852 | n_estimators=100, max_depth=7, learning_rate=1, max_features="log2" | +| 0.8513 | n_estimators=700, max_depth=1, learning_rate=1, max_features="log2" | +| 0.846 | n_estimators=300, max_depth=3, learning_rate=1, max_features="log2" | +| 0.8426 | n_estimators=500, max_depth=5, learning_rate=1, max_features="log2" | +| 0.8414 | n_estimators=700, max_depth=5, learning_rate=1, max_features="log2" | +| 0.8385 | n_estimators=700, max_depth=7, learning_rate=1, max_features="log2" | +| 0.8319 | n_estimators=300, max_depth=7, learning_rate=1, max_features="log2" | +| 0.8203 | n_estimators=100, max_depth=3, learning_rate=1, max_features="log2" | +| 0.8152 | n_estimators=500, max_depth=3, learning_rate=1, max_features="log2" | +| 0.8057 | n_estimators=500, max_depth=1, learning_rate=1, max_features="log2" | +| 0.779 | n_estimators=500, max_depth=7, learning_rate=1, max_features="log2" | + +## GaussianNB +| accuracy.macro | params | +|-----------------:|:---------| +| 0.8453 | |