% Python_3.5.2.
% Sklearn_0.18.1.
% NumPy_1.13.1.
% SciPy_0.19.1.
% Sun Jul 16 07:51:23 2017
% Created by run_task()
@RELATION openml_task_9985_predictions

@ATTRIBUTE repeat NUMERIC
@ATTRIBUTE fold NUMERIC
@ATTRIBUTE sample NUMERIC
@ATTRIBUTE row_id NUMERIC
@ATTRIBUTE confidence.1 NUMERIC
@ATTRIBUTE confidence.2 NUMERIC
@ATTRIBUTE confidence.3 NUMERIC
@ATTRIBUTE confidence.4 NUMERIC
@ATTRIBUTE confidence.5 NUMERIC
@ATTRIBUTE confidence.6 NUMERIC
@ATTRIBUTE prediction {1, 2, 3, 4, 5, 6}
@ATTRIBUTE correct {1, 2, 3, 4, 5, 6}

@DATA
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0,9,0,2101,0.166038391449,0.162604310511,0.163102546671,0.170952241202,0.168966521444,0.168335988722,4,1
0,9,0,4797,0.171213992115,0.163094513247,0.163656049651,0.169921719218,0.164384250808,0.167729474961,6,1
0,9,0,5248,0.167482265611,0.16550063696,0.167175193604,0.164313574592,0.164475430753,0.17105289848,6,2
0,9,0,4902,0.16957028726,0.166292371348,0.167488739663,0.165613356026,0.164998911549,0.166036334155,1,2
0,9,0,3670,0.167723298332,0.164983845874,0.167074651849,0.16507494108,0.165804153018,0.169339109847,6,2
0,9,0,4827,0.167853464498,0.165926737586,0.167986727318,0.164329010049,0.164701945573,0.169202114976,6,2
0,9,0,934,0.167653522687,0.165973584702,0.167636177101,0.164309350105,0.164470201157,0.169957164248,6,2
0,9,0,4096,0.167729363899,0.165047743073,0.16624735045,0.165450679154,0.165630604397,0.169894259027,6,2
0,9,0,2543,0.167765166365,0.164966658739,0.166505638605,0.165494266511,0.16673608246,0.16853218732,6,2
0,9,0,1668,0.167953323574,0.165519261857,0.167773821294,0.164379889204,0.164673038276,0.169700665795,6,2
0,9,0,3212,0.167902751086,0.165018412752,0.166454676098,0.165561797478,0.166492211329,0.168570151258,6,2
0,9,0,1095,0.168301059165,0.165661045309,0.167372432209,0.16436277215,0.164646789424,0.169655901743,6,2
0,9,0,120,0.167948248516,0.165511392116,0.167783149874,0.164381716636,0.164699073333,0.169676419525,6,2
0,9,0,644,0.167968765318,0.165126693556,0.16756290381,0.164546234031,0.165106755851,0.169688647434,6,2
0,9,0,224,0.16785613988,0.165919111235,0.1678814687,0.164378337676,0.164748115085,0.169216827424,6,2
0,9,0,3509,0.165933740189,0.16564358273,0.164548018192,0.164560547461,0.163408619436,0.175905491992,6,2
0,9,0,4831,0.16735506351,0.164998050585,0.166564760959,0.164865133218,0.165430448948,0.170786542779,6,2
0,9,0,5186,0.167733210109,0.165790365509,0.167414817739,0.165120154605,0.165341635004,0.168599817033,6,2
0,9,0,5518,0.168777133224,0.166683756357,0.170128122577,0.164451600019,0.164111189831,0.165848197991,3,2
0,9,0,3784,0.171110030042,0.163114310902,0.163747452601,0.169888390929,0.164425438411,0.167714377116,6,2
0,9,0,5023,0.167733126917,0.165334659117,0.168143655087,0.164465969434,0.164603239622,0.169719349823,6,2
0,9,0,4317,0.168342674505,0.166607676374,0.168523987917,0.164518994176,0.164288205007,0.167718462021,6,2
0,9,0,1268,0.167942437159,0.164877579301,0.166251890328,0.165811362891,0.166470902767,0.168645827555,6,2
0,9,0,5257,0.167650999701,0.165981679898,0.167631883212,0.164310349887,0.164456357966,0.169968729336,6,2
0,9,0,5775,0.167929096109,0.165523737349,0.16778566296,0.164376450534,0.164668872979,0.169716180069,6,2
0,9,0,3976,0.167566217139,0.164968536615,0.166265937941,0.166014174568,0.167325894105,0.167859239632,1,2
0,9,0,674,0.167830097238,0.165916587035,0.168028408541,0.164311156423,0.164751728215,0.169162022548,6,2
0,9,0,5339,0.167495517382,0.165526536961,0.167136007596,0.164325621945,0.164449035343,0.171067280773,6,2
0,9,0,1756,0.166178862519,0.168029548218,0.163499246339,0.168845517237,0.163052756677,0.170394069009,6,2
0,9,0,3069,0.1678936404,0.165440943516,0.167586426081,0.164594330846,0.165247727994,0.169236931162,6,2
0,9,0,4123,0.167851347119,0.166010012525,0.167978527161,0.164345721059,0.164711626344,0.169102765792,6,2
0,9,0,802,0.167832219929,0.166022408962,0.167981057353,0.164340469981,0.164681437588,0.169142406188,6,2
0,9,0,3650,0.166178862519,0.168029548218,0.163499246339,0.168845517237,0.163052756677,0.170394069009,6,2
0,9,0,5888,0.167215219343,0.164853572449,0.166010662697,0.165029187095,0.165281846787,0.171609511629,6,2
0,9,0,4701,0.167419221015,0.165933550434,0.167387551134,0.164255776777,0.164540537241,0.170463363399,6,2
0,9,0,4075,0.165912180823,0.165633386979,0.164531365223,0.164538301963,0.163386683135,0.175998081877,6,2
0,9,0,3401,0.16813165568,0.164977682784,0.167303018363,0.16465335129,0.164910042772,0.170024249111,6,2
0,9,0,3803,0.167316562381,0.165100680159,0.166141870339,0.165174481205,0.165600872267,0.170665533649,6,2
0,9,0,2594,0.167603055728,0.164934977814,0.166277771886,0.165882418133,0.16719232125,0.16810945519,6,2
0,9,0,1435,0.163757466962,0.162441393272,0.162355800417,0.163744624101,0.161823213245,0.185877502003,6,2
0,9,0,1682,0.166178862519,0.168029548218,0.163499246339,0.168845517237,0.163052756677,0.170394069009,6,2
0,9,0,5175,0.166842258337,0.16752560724,0.163860783523,0.167980574565,0.163795433192,0.169995343143,6,2
0,9,0,1768,0.166185775176,0.167185206528,0.164973591073,0.169193591775,0.162771662834,0.169690172614,6,2
0,9,0,4953,0.169569518293,0.16630451282,0.167487699067,0.165614782807,0.16499470997,0.166028777042,1,2
0,9,0,5516,0.165938274874,0.165629958591,0.164535743283,0.164565038501,0.163407352791,0.175923631961,6,2
0,9,0,4500,0.169569518293,0.16630451282,0.167487699067,0.165614782807,0.16499470997,0.166028777042,1,2
0,9,0,5956,0.167949783784,0.165509908271,0.167750002869,0.164392025682,0.164670551508,0.169727727886,6,2
0,9,0,5449,0.168820696756,0.165581866833,0.166600983874,0.165793124989,0.166010346367,0.167192981182,1,2
0,9,0,3668,0.166842258337,0.16752560724,0.163860783523,0.167980574565,0.163795433192,0.169995343143,6,2
0,9,0,800,0.167923975177,0.165565968079,0.167848107277,0.164350180119,0.164541172375,0.169770596973,6,2
% 
% 
%