% Python_3.6.3.
% Sklearn_0.19.1.
% NumPy_1.13.3.
% SciPy_1.0.0.
% Sun May  6 13:13:42 2018
% Created by run_task()
@RELATION openml_task_3669_predictions

@ATTRIBUTE repeat NUMERIC
@ATTRIBUTE fold NUMERIC
@ATTRIBUTE sample NUMERIC
@ATTRIBUTE row_id NUMERIC
@ATTRIBUTE confidence.P NUMERIC
@ATTRIBUTE confidence.N NUMERIC
@ATTRIBUTE prediction {P, N}
@ATTRIBUTE correct {P, N}

@DATA
0.0,0.0,0.0,68.0,0.2,0.8,N,N
0.0,0.0,0.0,42.0,0.3333333333333333,0.6666666666666666,N,N
0.0,0.0,0.0,43.0,0.7777777777777778,0.2222222222222222,P,N
0.0,0.0,0.0,35.0,0.7777777777777778,0.2222222222222222,P,N
0.0,0.0,0.0,18.0,0.7777777777777778,0.2222222222222222,P,P
0.0,0.0,0.0,23.0,0.7777777777777778,0.2222222222222222,P,P
0.0,0.0,0.0,21.0,0.7777777777777778,0.2222222222222222,P,P
0.0,1.0,0.0,53.0,0.0,1.0,N,N
0.0,1.0,0.0,63.0,0.0,1.0,N,N
0.0,1.0,0.0,60.0,0.0,1.0,N,N
0.0,1.0,0.0,48.0,1.0,0.0,P,N
0.0,1.0,0.0,30.0,0.2857142857142857,0.7142857142857143,N,P
0.0,1.0,0.0,12.0,1.0,0.0,P,P
0.0,1.0,0.0,7.0,1.0,0.0,P,P
0.0,2.0,0.0,51.0,0.0,1.0,N,N
0.0,2.0,0.0,59.0,0.14285714285714285,0.8571428571428571,N,N
0.0,2.0,0.0,49.0,0.14285714285714285,0.8571428571428571,N,N
0.0,2.0,0.0,56.0,0.14285714285714285,0.8571428571428571,N,N
0.0,2.0,0.0,3.0,0.7435897435897436,0.2564102564102564,P,P
0.0,2.0,0.0,9.0,0.7435897435897436,0.2564102564102564,P,P
0.0,2.0,0.0,20.0,0.1,0.9,N,P
0.0,3.0,0.0,36.0,0.0,1.0,N,N
0.0,3.0,0.0,41.0,0.8333333333333334,0.16666666666666666,P,N
0.0,3.0,0.0,34.0,0.8333333333333334,0.16666666666666666,P,N
0.0,3.0,0.0,37.0,0.14285714285714285,0.8571428571428571,N,N
0.0,3.0,0.0,32.0,0.8333333333333334,0.16666666666666666,P,P
0.0,3.0,0.0,4.0,0.8333333333333334,0.16666666666666666,P,P
0.0,3.0,0.0,26.0,0.8333333333333334,0.16666666666666666,P,P
0.0,4.0,0.0,69.0,0.0,1.0,N,N
0.0,4.0,0.0,46.0,0.7941176470588235,0.20588235294117646,P,N
0.0,4.0,0.0,39.0,0.7941176470588235,0.20588235294117646,P,N
0.0,4.0,0.0,55.0,0.25,0.75,N,N
0.0,4.0,0.0,10.0,0.7941176470588235,0.20588235294117646,P,P
0.0,4.0,0.0,22.0,0.7941176470588235,0.20588235294117646,P,P
0.0,4.0,0.0,24.0,0.7941176470588235,0.20588235294117646,P,P
0.0,5.0,0.0,67.0,0.8387096774193549,0.16129032258064516,P,N
0.0,5.0,0.0,45.0,0.0,1.0,N,N
0.0,5.0,0.0,61.0,0.0,1.0,N,N
0.0,5.0,0.0,40.0,0.29411764705882354,0.7058823529411765,N,N
0.0,5.0,0.0,8.0,0.0,1.0,N,P
0.0,5.0,0.0,16.0,0.8387096774193549,0.16129032258064516,P,P
0.0,5.0,0.0,17.0,0.29411764705882354,0.7058823529411765,N,P
0.0,6.0,0.0,54.0,0.3333333333333333,0.6666666666666666,N,N
0.0,6.0,0.0,66.0,0.0,1.0,N,N
0.0,6.0,0.0,62.0,0.0,1.0,N,N
0.0,6.0,0.0,13.0,0.7931034482758621,0.20689655172413793,P,P
0.0,6.0,0.0,33.0,0.7931034482758621,0.20689655172413793,P,P
0.0,6.0,0.0,11.0,0.7931034482758621,0.20689655172413793,P,P
0.0,6.0,0.0,27.0,0.7931034482758621,0.20689655172413793,P,P
0.0,7.0,0.0,38.0,0.8571428571428571,0.14285714285714285,P,N
0.0,7.0,0.0,58.0,0.29411764705882354,0.7058823529411765,N,N
0.0,7.0,0.0,50.0,0.8571428571428571,0.14285714285714285,P,N
0.0,7.0,0.0,1.0,0.29411764705882354,0.7058823529411765,N,P
0.0,7.0,0.0,28.0,0.8571428571428571,0.14285714285714285,P,P
0.0,7.0,0.0,2.0,0.8571428571428571,0.14285714285714285,P,P
0.0,7.0,0.0,15.0,0.8571428571428571,0.14285714285714285,P,P
0.0,8.0,0.0,47.0,0.8333333333333334,0.16666666666666666,P,N
0.0,8.0,0.0,52.0,0.25,0.75,N,N
0.0,8.0,0.0,65.0,0.14285714285714285,0.8571428571428571,N,N
0.0,8.0,0.0,6.0,0.8333333333333334,0.16666666666666666,P,P
0.0,8.0,0.0,31.0,0.25,0.75,N,P
0.0,8.0,0.0,29.0,0.8333333333333334,0.16666666666666666,P,P
0.0,8.0,0.0,5.0,0.25,0.75,N,P
0.0,9.0,0.0,64.0,0.0,1.0,N,N
0.0,9.0,0.0,44.0,0.5,0.5,P,N
0.0,9.0,0.0,57.0,0.0,1.0,N,N
0.0,9.0,0.0,25.0,1.0,0.0,P,P
0.0,9.0,0.0,0.0,1.0,0.0,P,P
0.0,9.0,0.0,19.0,1.0,0.0,P,P
0.0,9.0,0.0,14.0,0.0,1.0,N,P
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