% Python_3.6.0.
% Sklearn_0.18.1.
% NumPy_1.13.0.
% SciPy_0.19.1.
% Mon Jul 17 12:50:19 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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0,9,0,801,0.0203260628261,0.979673937174,1,1
0,9,0,365,0.0,1.0,1,1
0,9,0,681,0.112675976012,0.887324023988,1,1
0,9,0,776,0.175209036686,0.824790963314,1,1
0,9,0,914,0.100277124912,0.899722875088,1,1
0,9,0,25,0.0100396825397,0.98996031746,1,1
0,9,0,766,0.15552519078,0.84447480922,1,1
0,9,0,592,0.0788888888889,0.921111111111,1,1
0,9,0,363,0.00222222222222,0.997777777778,1,1
0,9,0,591,0.0770707070707,0.922929292929,1,1
0,9,0,485,0.0438227041414,0.956177295859,1,1
0,9,0,707,0.170053935412,0.829946064588,1,1
0,9,0,501,0.100229362409,0.899770637591,1,1
0,9,0,104,0.0433048336073,0.956695166393,1,1
0,9,0,213,0.0412444353033,0.958755564697,1,1
0,9,0,429,0.0,1.0,1,1
0,9,0,757,0.336460482562,0.663539517438,1,1
0,9,0,302,0.00470127357163,0.995298726428,1,1
0,9,0,841,0.00528846153846,0.994711538462,1,1
0,9,0,121,0.017167022792,0.982832977208,1,1
0,9,0,878,0.235717924708,0.764282075292,1,1
0,9,0,174,0.0798091597696,0.92019084023,1,1
0,9,0,18,0.111265682735,0.888734317265,1,1
0,9,0,11,0.317481871766,0.682518128234,1,1
0,9,0,472,0.692001436635,0.307998563365,0,1
0,9,0,584,0.0686666666667,0.931333333333,1,1
0,9,0,576,0.048,0.952,1,1
0,9,0,503,0.0190634920635,0.980936507937,1,1
0,9,0,206,0.0252419870979,0.974758012902,1,1
0,9,0,132,0.0173535353535,0.982646464646,1,1
0,9,0,984,0.27737351454,0.72262648546,1,1
0,9,0,909,0.174477014239,0.825522985761,1,1
0,9,0,492,0.122306616238,0.877693383762,1,1
0,9,0,937,0.0118650793651,0.988134920635,1,1
0,9,0,408,0.00843304843305,0.991566951567,1,1
0,9,0,892,0.109605605506,0.890394394494,1,1
0,9,0,52,0.17251756685,0.82748243315,1,1
0,9,0,542,0.21376775339,0.78623224661,1,1
0,9,0,239,0.0106947376239,0.989305262376,1,1
0,9,0,816,0.0110909090909,0.988909090909,1,1
0,9,0,82,0.0552522893773,0.944747710623,1,1
0,9,0,232,0.0327289377289,0.967271062271,1,1
0,9,0,516,0.0528644134491,0.947135586551,1,1
0,9,0,554,0.041232010582,0.958767989418,1,1
0,9,0,448,0.260315101211,0.739684898789,1,1
0,9,0,846,0.0103333333333,0.989666666667,1,1
0,9,0,217,0.0253789249966,0.974621075003,1,1
0,9,0,983,0.255904298769,0.744095701231,1,1
0,9,0,362,0.00222222222222,0.997777777778,1,1
0,9,0,356,0.0158123846827,0.984187615317,1,1
0,9,0,85,0.0384665750916,0.961533424908,1,1
0,9,0,22,0.0516581027668,0.948341897233,1,1
0,9,0,190,0.0545001485443,0.945499851456,1,1
0,9,0,92,0.013109178744,0.986890821256,1,1
0,9,0,941,0.146288209773,0.853711790227,1,1
0,9,0,549,0.149313743443,0.850686256557,1,1
0,9,0,716,0.171128912436,0.828871087564,1,1
0,9,0,299,0.0,1.0,1,1
0,9,0,551,0.108345416661,0.891654583339,1,1
0,9,0,661,0.0445112853661,0.955488714634,1,1
0,9,0,102,0.0748034375469,0.925196562453,1,1
0,9,0,1010,0.15306575678,0.84693424322,1,1
0,9,0,530,0.0466816139524,0.953318386048,1,1
0,9,0,290,0.00325396825397,0.996746031746,1,1
0,9,0,208,0.0252419870979,0.974758012902,1,1
0,9,0,988,0.178591264082,0.821408735918,1,1
0,9,0,665,0.007875,0.992125,1,1
0,9,0,803,0.0160809190809,0.983919080919,1,1
0,9,0,447,0.387633306778,0.612366693222,1,1
0,9,0,786,0.0286513720198,0.97134862798,1,1
0,9,0,381,0.0,1.0,1,1
0,9,0,181,0.0969521981015,0.903047801899,1,1
0,9,0,34,0.0184572649573,0.981542735043,1,1
0,9,0,735,0.322764963337,0.677235036663,1,1
0,9,0,148,0.0120757575758,0.987924242424,1,1
0,9,0,98,0.118202781867,0.881797218133,1,1
0,9,0,662,0.148792101904,0.851207898096,1,1
0,9,0,884,0.205880608281,0.794119391719,1,1
0,9,0,575,0.112186147186,0.887813852814,1,1
0,9,0,461,0.681465958607,0.318534041393,0,1
0,9,0,739,0.280450905228,0.719549094772,1,1
% 
% 
%