% Python_3.6.0.
% Sklearn_0.18.1.
% NumPy_1.13.0.
% SciPy_0.19.1.
% Mon Jul 17 14:08:48 2017
% Created by run_task()
@RELATION openml_task_3950_predictions

@ATTRIBUTE repeat NUMERIC
@ATTRIBUTE fold NUMERIC
@ATTRIBUTE sample NUMERIC
@ATTRIBUTE row_id NUMERIC
@ATTRIBUTE confidence.0 NUMERIC
@ATTRIBUTE confidence.1 NUMERIC
@ATTRIBUTE prediction {0, 1}
@ATTRIBUTE correct {0, 1}

@DATA
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0,9,0,664,0.0397714285714,0.960228571429,1,1
0,9,0,669,0.0228,0.9772,1,1
0,9,0,801,0.0210256410256,0.978974358974,1,1
0,9,0,365,0.0,1.0,1,1
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0,9,0,776,0.164354327744,0.835645672256,1,1
0,9,0,914,0.0821192780903,0.91788072191,1,1
0,9,0,25,0.0211965811966,0.978803418803,1,1
0,9,0,766,0.120818729821,0.879181270179,1,1
0,9,0,592,0.0838752136752,0.916124786325,1,1
0,9,0,363,0.00153846153846,0.998461538462,1,1
0,9,0,591,0.0831609279609,0.916839072039,1,1
0,9,0,485,0.0404623154623,0.959537684538,1,1
0,9,0,707,0.185496098999,0.814503901001,1,1
0,9,0,501,0.0678938779995,0.932106122001,1,1
0,9,0,104,0.0980443208752,0.901955679125,1,1
0,9,0,213,0.0334879826056,0.966512017394,1,1
0,9,0,429,0.00230769230769,0.997692307692,1,1
0,9,0,757,0.302512633884,0.697487366116,1,1
0,9,0,302,0.00222222222222,0.997777777778,1,1
0,9,0,841,0.0,1.0,1,1
0,9,0,121,0.0150854700855,0.984914529915,1,1
0,9,0,878,0.154432423352,0.845567576648,1,1
0,9,0,174,0.0583149873003,0.9416850127,1,1
0,9,0,18,0.104330897436,0.895669102564,1,1
0,9,0,11,0.367157552326,0.632842447674,1,1
0,9,0,472,0.728347122734,0.271652877266,0,1
0,9,0,584,0.0915384615385,0.908461538462,1,1
0,9,0,576,0.0594805194805,0.940519480519,1,1
0,9,0,503,0.0341376941899,0.96586230581,1,1
0,9,0,206,0.0361480015083,0.963851998492,1,1
0,9,0,132,0.005625,0.994375,1,1
0,9,0,984,0.162191626408,0.837808373592,1,1
0,9,0,909,0.135486666172,0.864513333828,1,1
0,9,0,492,0.0999200053995,0.9000799946,1,1
0,9,0,937,0.0200798534799,0.97992014652,1,1
0,9,0,408,0.0074188034188,0.992581196581,1,1
0,9,0,892,0.0842071491849,0.915792850815,1,1
0,9,0,52,0.107190094167,0.892809905833,1,1
0,9,0,542,0.21652106938,0.78347893062,1,1
0,9,0,239,0.00760683760684,0.992393162393,1,1
0,9,0,816,0.0,1.0,1,1
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0,9,0,516,0.0656711050529,0.934328894947,1,1
0,9,0,554,0.0272767571729,0.972723242827,1,1
0,9,0,448,0.174040167657,0.825959832343,1,1
0,9,0,846,0.0,1.0,1,1
0,9,0,217,0.0361764945838,0.963823505416,1,1
0,9,0,983,0.213980932842,0.786019067158,1,1
0,9,0,362,0.000769230769231,0.999230769231,1,1
0,9,0,356,0.00222222222222,0.997777777778,1,1
0,9,0,85,0.0958170710009,0.904182928999,1,1
0,9,0,22,0.0279694144864,0.972030585514,1,1
0,9,0,190,0.0192798378092,0.980720162191,1,1
0,9,0,92,0.0553621386242,0.944637861376,1,1
0,9,0,941,0.183817283384,0.816182716616,1,1
0,9,0,549,0.161206889856,0.838793110144,1,1
0,9,0,716,0.169904840392,0.830095159608,1,1
0,9,0,299,0.0,1.0,1,1
0,9,0,551,0.112741903779,0.887258096221,1,1
0,9,0,661,0.0366438502674,0.963356149733,1,1
0,9,0,102,0.0747260654711,0.925273934529,1,1
0,9,0,1010,0.114355533462,0.885644466538,1,1
0,9,0,530,0.047560870502,0.952439129498,1,1
0,9,0,290,0.0181112637363,0.981888736264,1,1
0,9,0,208,0.0361480015083,0.963851998492,1,1
0,9,0,988,0.165418236067,0.834581763933,1,1
0,9,0,665,0.0,1.0,1,1
0,9,0,803,0.0114957264957,0.988504273504,1,1
0,9,0,447,0.437367513614,0.562632486386,1,1
0,9,0,786,0.00230769230769,0.997692307692,1,1
0,9,0,381,0.005,0.995,1,1
0,9,0,181,0.027761387632,0.972238612368,1,1
0,9,0,34,0.0,1.0,1,1
0,9,0,735,0.265443774781,0.734556225219,1,1
0,9,0,148,0.002,0.998,1,1
0,9,0,98,0.0918192918193,0.908180708181,1,1
0,9,0,662,0.0204524886878,0.979547511312,1,1
0,9,0,884,0.139397670956,0.860602329044,1,1
0,9,0,575,0.122253413253,0.877746586747,1,1
0,9,0,461,0.696708873486,0.303291126514,0,1
0,9,0,739,0.27377003085,0.72622996915,1,1
% 
% 
%