521110
64
Rafael Gomes Mantovani
9956
Supervised Classification
2596
classif.LiblineaRL1LogReg(6)
3500
seed
1
2596
kind
Mersenne-Twister
2596
normal.kind
Inversion
2596
study_7
1493
one-hundred-plants-texture
https://www.openml.org/data/download/1592285/phpoOxxNn
-1
1791581
description
https://api.openml.org/data/download/1791581/file37d21949bd86
-1
1791582
predictions
https://api.openml.org/data/download/1791582/file37d2402306d1
area_under_roc_curve
0.589703 [0.5,0.5,0.5,0.79955,0.498421,0.5,0.5,0.499368,0.559657,0.5,0.5,0.497789,0.5,0.617104,0.5,0.796707,0.61584,0.5,0.5,0.558078,0.5,0.5,0.561552,0.5,0.5,0.829852,0.72789,0.497157,0.771774,0.928024,0.5,0.992419,0.5,0.763246,0.499368,0.5,0.499684,0.608891,0.5,0.856996,0.5,0.748105,0.525249,0.5,0.5,0.5,0.5,0.499368,0.5,0.499684,0.882877,0.5,0.5,0.663811,0.5,0.745578,0.967171,0.5,0.5,0.829536,0.980733,0.769563,0.499684,0.770511,0.5,0.49621,0.5,0.499052,0.497157,0.5,0.5,0.5,0.527144,0.648669,0.499684,0.5,0.5,0.9558,0.5,0.5,0.5,0.5,0.5,0.499684,0.528407,0.913495,0.5,0.5,0.5,0.863629,0.686552,0.5,0.5,0.92834,0.58996,0.5,0.499684,0.982944,0.5,0.48705]
average_cost
0
f_measure
0.10239 [0,0,0,0.298507,0,0,0,0,0.148148,0,0,0,0,0.177778,0,0.263158,0.163265,0,0,0.125,0,0,0.190476,0,0,0.309859,0.170213,0,0.327273,0.466667,0,0.571429,0,0.219512,0,0,0,0.112676,0,0.282353,0,0.533333,0.055556,0,0,0,0,0,0,0,0.252427,0,0,0.123711,0,0.421053,0.833333,0,0,0.305556,0.344086,0.290323,0,0.305085,0,0,0,0,0,0,0,0,0.066667,0.222222,0,0,0,0.416667,0,0,0,0,0,0,0.076923,0.264151,0,0,0,0.375,0.48,0,0,0.474576,0.193548,0,0,0.372093,0,0]
kappa
0.179406
kb_relative_information_score
297.128115
mean_absolute_error
0.016248
mean_prior_absolute_error
0.0198
number_of_instances
1599 [15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16]
precision
0.081741 [0,0,0,0.196078,0,0,0,0,0.181818,0,0,0,0,0.137931,0,0.166667,0.121212,0,0,0.125,0,0,0.4,0,0,0.2,0.102564,0,0.230769,0.318182,0,0.4,0,0.136364,0,0,0,0.072727,0,0.173913,0,0.571429,0.05,0,0,0,0,0,0,0,0.149425,0,0,0.074074,0,0.363636,0.75,0,0,0.196429,0.207792,0.195652,0,0.209302,0,0,0,0,0,0,0,0,0.071429,0.172414,0,0,0,0.267857,0,0,0,0,0,0,0.1,0.155556,0,0,0,0.25,0.666667,0,0,0.325581,0.2,0,0,0.228571,0,0]
predictive_accuracy
0.187617
prior_entropy
6.643831
recall
0.187617 [0,0,0,0.625,0,0,0,0,0.125,0,0,0,0,0.25,0,0.625,0.25,0,0,0.125,0,0,0.125,0,0,0.6875,0.5,0,0.5625,0.875,0,1,0,0.5625,0,0,0,0.25,0,0.75,0,0.5,0.0625,0,0,0,0,0,0,0,0.8125,0,0,0.375,0,0.5,0.9375,0,0,0.6875,1,0.5625,0,0.5625,0,0,0,0,0,0,0,0,0.0625,0.3125,0,0,0,0.9375,0,0,0,0,0,0,0.0625,0.875,0,0,0,0.75,0.375,0,0,0.875,0.1875,0,0,1,0,0]
relative_absolute_error
0.820589
root_mean_prior_squared_error
0.099499
root_mean_squared_error
0.127466
root_relative_squared_error
1.281085
total_cost
0
usercpu_time_millis
5.48
usercpu_time_millis_testing
0.0199999999999987
usercpu_time_millis_training
5.46
area_under_roc_curve
0.599574 [0.5,0.5,0.5,0.981013,0.5,0.5,0.5,0.496855,0.5,0.5,0.5,0.493671,0.5,0.487342,0.5,0.740506,0.743671,0.5,0.5,0.5,0.5,0.5,0.75,0.5,0.5,0.987421,0.971698,0.5,0.993671,0.496855,0.5,0.996835,0.5,0.481132,0.5,0.5,0.496855,0.471698,0.5,0.737342,0.5,0.993711,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.977987,0.5,0.5,0.731013,0.5,0.75,1,0.5,0.5,0.984277,0.981132,0.731013,0.5,0.490566,0.5,0.5,0.5,0.5,0.493711,0.5,0.5,0.5,0.993711,0.493711,0.5,0.5,0.5,0.990506,0.5,0.5,0.5,0.5,0.5,0.5,0.996855,0.987342,0.5,0.5,0.5,0.740506,0.746835,0.5,0.5,0.990506,0.740506,0.5,0.496855,0.977987,0.5,0.484277]
area_under_roc_curve
0.571252 [0.5,0.5,0.5,0.743671,0.5,0.5,0.5,0.496855,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.727848,0.496835,0.5,0.5,0.737342,0.5,0.5,0.5,0.5,0.5,0.487342,0.468553,0.5,0.740506,0.740506,0.5,0.990506,0.5,0.974843,0.5,0.5,0.5,0.490566,0.5,0.737342,0.5,0.496855,0.496855,0.5,0.5,0.5,0.5,0.496855,0.5,0.5,0.965409,0.5,0.5,0.484177,0.5,0.990506,0.993711,0.5,0.5,0.981132,0.977987,0.490506,0.5,0.487421,0.5,0.496835,0.5,0.5,0.493711,0.5,0.5,0.5,0.496855,0.984277,0.5,0.5,0.5,0.993671,0.5,0.5,0.5,0.5,0.5,0.5,0.487421,0.981013,0.5,0.5,0.5,0.990506,0.5,0.5,0.5,0.984277,0.5,0.5,0.5,0.981132,0.5,0.487421]
area_under_roc_curve
0.593145 [0.5,0.5,0.5,0.737342,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.496855,0.5,0.737342,0.5,0.993671,0.987342,0.5,0.5,0.496855,0.5,0.5,0.5,0.5,0.5,0.987342,0.968553,0.5,0.996835,0.990506,0.5,0.993711,0.5,0.734177,0.5,0.5,0.5,0.734177,0.5,0.990566,0.5,0.996855,0.496855,0.5,0.5,0.5,0.5,0.5,0.5,0.496855,0.971698,0.5,0.5,0.493671,0.5,0.5,1,0.5,0.5,0.474843,0.977848,0.990566,0.5,0.990566,0.5,0.496835,0.5,0.496855,0.493711,0.5,0.5,0.5,0.487342,0.490566,0.5,0.5,0.5,0.987421,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.718354,0.5,0.5,0.5,0.984277,1,0.5,0.5,0.987421,0.5,0.5,0.5,0.987342,0.5,0.477987]
area_under_roc_curve
0.589981 [0.5,0.5,0.5,0.984177,0.5,0.5,0.5,0.5,0.487421,0.5,0.5,0.5,0.5,0.5,0.5,0.724684,0.496835,0.5,0.5,0.987421,0.5,0.5,0.5,0.5,0.5,0.737342,0.977987,0.493671,0.490506,0.984177,0.5,0.993711,0.5,0.990506,0.5,0.5,0.5,0.743671,0.5,0.981132,0.5,0.496855,0.487421,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.977987,0.5,0.5,0.724684,0.5,0.743671,1,0.5,0.5,0.977987,0.984177,0.5,0.496855,0.481132,0.5,0.496855,0.5,0.5,0.5,0.5,0.5,0.5,0.496835,1,0.5,0.5,0.5,0.971698,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.984177,0.5,0.5,0.5,0.996855,1,0.5,0.5,0.990566,0.987421,0.5,0.5,0.977848,0.5,0.493711]
area_under_roc_curve
0.573959 [0.5,0.5,0.5,0.984277,0.493711,0.5,0.5,0.5,1,0.5,0.5,0.496855,0.5,0.496835,0.5,0.974843,0.5,0.5,0.5,0.487421,0.5,0.5,0.5,0.5,0.5,0.743671,0.731013,0.5,0.731013,0.990506,0.5,0.993711,0.5,0.737342,0.496835,0.5,0.5,0.487342,0.5,0.465409,0.5,0.996835,0.490506,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.734177,0.5,0.5,0.468553,0.5,0.5,1,0.5,0.5,0.737342,0.974684,0.990566,0.5,0.740506,0.5,0.493711,0.5,0.5,0.496835,0.5,0.5,0.5,0.5,0.731013,0.5,0.5,0.5,0.993711,0.5,0.5,0.5,0.5,0.5,0.496855,0.5,0.968354,0.5,0.5,0.5,0.487421,0.5,0.5,0.5,0.487421,0.5,0.5,0.5,0.984177,0.5,0.490506]
area_under_roc_curve
0.599037 [0.5,0.5,0.5,0.493711,0.493711,0.5,0.5,0.5,1,0.5,0.5,0.5,0.5,0.493671,0.5,0.968553,0.743671,0.5,0.5,0.496855,0.5,0.5,0.496835,0.5,0.5,0.990506,0.990506,0.487421,0.987342,0.990506,0.5,0.996855,0.5,0.724684,0.5,0.5,0.5,0.490506,0.5,0.990566,0.5,0.5,0.740506,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.971519,0.5,0.5,0.968553,0.5,0.987342,0.75,0.5,0.5,0.731013,0.981013,0.487421,0.5,0.740506,0.5,0.496855,0.5,0.496835,0.5,0.5,0.5,0.5,0.5,0.496835,0.5,0.5,0.5,0.987421,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.971519,0.5,0.5,0.5,0.993711,0.5,0.5,0.5,0.996835,0.490566,0.5,0.5,0.981013,0.5,0.493671]
area_under_roc_curve
0.602241 [0.5,0.5,0.5,0.481132,0.496855,0.5,0.5,0.5,0.496835,0.5,0.5,0.496835,0.5,0.990566,0.5,0.490566,0.493711,0.5,0.5,0.5,0.5,0.5,0.496855,0.5,0.5,0.740506,0.474684,0.5,0.484277,0.981013,0.5,0.993671,0.5,0.984177,0.5,0.5,0.5,0.737342,0.5,0.987342,0.5,1,0.496835,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.987342,0.5,0.5,0.968553,0.5,0.5,1,0.5,0.5,0.740506,0.977848,0.743671,0.5,0.746835,0.5,0.484277,0.5,0.5,0.496835,0.5,0.5,0.5,0.493671,0.490506,0.5,0.5,0.5,0.984177,0.5,0.5,0.5,0.5,0.5,0.5,0.496835,0.965409,0.5,0.5,0.5,0.990506,0.75,0.5,0.5,0.990506,0.993711,0.5,0.5,0.987342,0.5,0.490506]
area_under_roc_curve
0.57376 [0.5,0.5,0.5,0.987421,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.493671,0.5,0.493711,0.5,0.993711,0.468553,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.974843,0.487342,0.493711,0.496855,0.993711,0.5,0.990506,0.5,0.481013,0.5,0.5,0.5,0.477848,0.5,0.981013,0.5,0.5,0.493671,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.71519,0.5,0.5,0.468553,0.5,0.493711,0.996835,0.5,0.5,0.740506,0.990506,0.981013,0.5,0.981013,0.5,0.496835,0.5,0.496835,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.987342,0.5,0.5,0.5,0.5,0.5,0.5,0.496835,0.481132,0.5,0.5,0.5,0.727848,0.75,0.5,0.5,0.743671,0.5,0.5,0.5,0.977848,0.5,0.490506]
area_under_roc_curve
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area_under_roc_curve
0.587096 [0.5,0.5,0.5,0.740446,0.5,0.5,0.5,0.5,0.496815,0.5,0.5,0.5,0.5,0.987342,0.5,0.737261,0.481013,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.981013,0.737261,0.5,0.990506,0.993671,0.5,0.996815,0.5,0.481013,0.5,0.5,0.5,0.974684,0.5,0.714968,0.5,0.75,0.490446,0.5,0.5,0.5,0.5,0.496835,0.5,0.5,0.727707,0.5,0.5,0.724522,0.5,1,0.996815,0.5,0.5,0.993631,0.977848,0.734076,0.5,0.740446,0.5,0.5,0.5,0.5,0.496815,0.5,0.5,0.5,0.493671,0.496815,0.5,0.5,0.5,0.730892,0.5,0.5,0.5,0.5,0.5,0.5,0.496815,0.981013,0.5,0.5,0.5,0.984076,0.496815,0.5,0.5,0.990446,0.5,0.5,0.5,0.987342,0.5,0.487261]
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
f_measure
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f_measure
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f_measure
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f_measure
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f_measure
0.08249 [0,0,0,0.285714,0,0,0,0,1,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0,0,0.4,0.222222,0,0.222222,0.571429,0,0.5,0,0.285714,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,1,0,0,0.285714,0.333333,0.4,0,0.333333,0,0,0,0,0,0,0,0,0,0.222222,0,0,0,0.5,0,0,0,0,0,0,0,0.285714,0,0,0,0,0,0,0,0,0,0,0,0.444444,0,0]
f_measure
0.108546 [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.166667,0.4,0,0,0,0,0,0,0,0,0.571429,0.571429,0,0.5,0.571429,0,0.666667,0,0.181818,0,0,0,0,0,0.4,0,0,0.333333,0,0,0,0,0,0,0,0.307692,0,0,0.166667,0,0.5,0.666667,0,0,0.222222,0.4,0,0,0.333333,0,0,0,0,0,0,0,0,0,0,0,0,0,0.333333,0,0,0,0,0,0,0,0.307692,0,0,0,0.5,0,0,0,0.8,0,0,0,0.4,0,0]
f_measure
0.126142 [0,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0.333333,0,0,0,0.4,0,0.666667,0,0.444444,0,0,0,0.285714,0,0.5,0,1,0,0,0,0,0,0,0,0,0.5,0,0,0.166667,0,0,1,0,0,0.333333,0.363636,0.4,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0.444444,0,0,0,0,0,0,0,0.153846,0,0,0,0.571429,0.666667,0,0,0.571429,0.5,0,0,0.5,0,0]
f_measure
0.08145 [0,0,0,0.333333,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0.5,0,0.571429,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0.142857,0,0,0,0,0,0.8,0,0,0.333333,0.571429,0.4,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0.2,0.666667,0,0,0.4,0,0,0,0.363636,0,0]
f_measure
0.126773 [0,0,0,0.285714,0,0,0,0,0,0,0,0,0,0.285714,0,0.333333,0,0,0,0,0,0,0.666667,0,0,0.25,0.166667,0,0,0.5,0,0.363636,0,0.25,0,0,0,0,0,0.444444,0,0.666667,0,0,0,0,0,0,0,0,0.5,0,0,0.222222,0,0.4,0.8,0,0,0.8,0.285714,0.444444,0,0.666667,0,0,0,0,0,0,0,0,0,0.571429,0,0,0,0.666667,0,0,0,0,0,0,0,0.181818,0,0,0,0.333333,0.5,0,0,0.8,0,0,0,0.333333,0,0]
f_measure
0.099731 [0,0,0,0.333333,0,0,0,0,0,0,0,0,0,0.333333,0,0.285714,0,0,0,0,0,0,0,0,0,0.25,0.285714,0,0.4,0.5,0,0.8,0,0,0,0,0,0.2,0,0.142857,0,0.666667,0,0,0,0,0,0,0,0,0.2,0,0,0.181818,0,1,0.8,0,0,0.666667,0.222222,0.25,0,0.333333,0,0,0,0,0,0,0,0,0,0,0,0,0,0.222222,0,0,0,0,0,0,0,0.25,0,0,0,0.444444,0,0,0,0.571429,0,0,0,0.333333,0,0]
kappa
0.198833
kappa
0.142294
kappa
0.186152
kappa
0.179875
kappa
0.147795
kappa
0.197916
kappa
0.2042
kappa
0.147357
kappa
0.21711
kappa
0.174092
kb_relative_information_score
32.718837
kb_relative_information_score
23.700299
kb_relative_information_score
30.714717
kb_relative_information_score
29.712658
kb_relative_information_score
24.702359
kb_relative_information_score
32.718708
kb_relative_information_score
33.720767
kb_relative_information_score
24.70223
kb_relative_information_score
35.724887
kb_relative_information_score
28.712653
mean_absolute_error
0.015875
mean_absolute_error
0.017
mean_absolute_error
0.016125
mean_absolute_error
0.01625
mean_absolute_error
0.016875
mean_absolute_error
0.015875
mean_absolute_error
0.01575
mean_absolute_error
0.016875
mean_absolute_error
0.0155
mean_absolute_error
0.016352
mean_prior_absolute_error
0.0198
mean_prior_absolute_error
0.0198
mean_prior_absolute_error
0.0198
mean_prior_absolute_error
0.0198
mean_prior_absolute_error
0.0198
mean_prior_absolute_error
0.0198
mean_prior_absolute_error
0.0198
mean_prior_absolute_error
0.0198
mean_prior_absolute_error
0.0198
mean_prior_absolute_error
0.0198
number_of_instances
160 [2,1,1,2,2,2,2,1,2,2,1,2,2,2,2,2,2,1,2,2,1,2,2,2,2,1,1,2,2,1,2,2,1,1,1,2,1,1,2,2,2,1,1,2,1,1,2,1,2,2,1,2,1,2,1,2,2,1,1,1,1,2,1,1,2,2,2,1,1,2,2,2,1,1,2,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1]
number_of_instances
160 [2,1,1,2,2,2,2,1,2,2,1,2,2,2,2,2,2,1,2,2,2,2,2,2,2,2,1,2,2,2,2,2,1,1,1,2,1,1,2,2,2,1,1,2,1,1,2,1,2,2,1,2,1,2,1,2,1,1,1,1,1,2,1,1,2,2,2,1,1,2,2,2,1,1,2,2,2,2,1,1,1,2,1,2,1,2,2,2,2,2,2,2,1,1,2,2,1,1,2,1]
number_of_instances
160 [2,1,2,2,2,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1,2,2,2,2,1,2,2,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,1,1,2,2,2,1,2,1,1,1,1,2,1,1,1,2,2,2,1,1,2,2,2,2,1,2,2,2,1,2,2,1,2,1,2,1,2,2,1,2,1,1,2,2,1,2,1,1,2,2,1]
number_of_instances
160 [2,1,2,2,2,1,2,2,1,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1,2,2,2,2,1,2,2,2,1,1,2,1,1,2,1,1,2,2,2,2,2,1,1,1,2,2,2,2,2,1,1,1,1,2,1,1,1,2,1,2,1,1,2,2,2,2,1,2,2,2,1,2,2,1,2,1,2,1,2,2,1,2,1,1,2,2,1,1,1,2,2,2,1]
number_of_instances
160 [2,2,2,1,1,1,1,2,1,1,2,1,1,2,1,1,2,2,2,1,2,1,2,1,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,1,2,2,2,2,2,2,2,2,1,1,2,2,2,1,2,2,1,2,2,2,2,1,2,2,1,1,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,2,1,1,2,2,2,1,2,1,1,2,2,1,1,1,2,2,2,2]
number_of_instances
160 [1,2,2,1,1,1,1,2,1,1,2,1,1,2,1,1,2,2,2,1,2,1,2,1,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,1,2,2,2,2,2,2,2,2,1,1,2,1,2,1,2,2,2,2,2,2,2,1,2,2,1,1,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,1,2,1,2,2,2,1,2,1,1,2,2,2,1,1,2,2,2,2]
number_of_instances
160 [1,2,2,1,1,2,1,2,2,1,2,2,1,1,1,1,1,2,1,2,2,1,1,2,2,2,2,1,1,2,1,2,2,2,2,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,1,2,1,2,1,2,2,2,2,2,2,2,2,1,1,1,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,1,2,1,1,2,1,2,2,1,2,2,1,2,2,2,1,2]
number_of_instances
160 [1,2,2,1,1,2,1,2,2,1,2,2,1,1,1,1,1,2,1,2,1,1,1,2,2,1,2,1,1,1,1,2,2,2,2,2,2,2,2,2,1,2,2,1,2,2,1,2,2,2,2,1,2,1,2,1,2,2,2,2,2,2,2,2,1,2,1,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,1,2,1,1,2,1,2,2,1,2,2,2,2,2,2,1,2]
number_of_instances
160 [1,2,1,2,2,2,2,2,2,2,1,2,2,1,2,2,1,1,1,2,1,2,1,2,2,1,2,2,1,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,1,1,1,1,2,2,2,1,1,2,2,1,2,2,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,2,1,1,2,1,2,2,2,1,1,2,1,2,2,1,1,2,2,2,2,1,1,2]
number_of_instances
159 [1,2,1,2,2,2,2,1,2,2,1,2,2,1,2,2,1,1,1,2,1,2,1,2,2,1,2,2,1,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,1,1,1,1,2,2,2,2,1,2,1,1,2,2,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,2,1,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,2,1,1,1,2]
precision
0.107902 [0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0.25,0.333333,0,0,0,0,0,1,0,0,0.2,0.1,0,0.5,0,0,0.666667,0,0,0,0,0,0,0,0.2,0,0.333333,0,0,0,0,0,0,0,0,0.125,0,0,0.142857,0,1,1,0,0,0.166667,0.142857,0.142857,0,0,0,0,0,0,0,0,0,0,0.333333,0,0,0,0,0.4,0,0,0,0,0,0,0.5,0.333333,0,0,0,0.25,0.5,0,0,0.4,0.25,0,0,0.125,0,0]
precision
0.049303 [0,0,0,0.333333,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0,0,0.2,0,0,0,0,0,0,0,0,0.25,0.25,0,0.4,0,0.111111,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0.083333,0,0,0,0,0.4,0.333333,0,0,0.142857,0.125,0,0,0,0,0,0,0,0,0,0,0,0,0.166667,0,0,0,0.5,0,0,0,0,0,0,0,0.25,0,0,0,0.4,0,0,0,0.166667,0,0,0,0.142857,0,0]
precision
0.072295 [0,0,0,0.2,0,0,0,0,0,0,0,0,0,0.2,0,0.5,0.333333,0,0,0,0,0,0,0,0,0.333333,0.090909,0,0.666667,0.4,0,0.333333,0,0.166667,0,0,0,0.166667,0,0.25,0,0.5,0,0,0,0,0,0,0,0,0.1,0,0,0,0,0,1,0,0,0,0.222222,0.25,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0.090909,0,0,0,0.166667,1,0,0,0.2,0,0,0,0.333333,0,0]
precision
0.067557 [0,0,0,0.285714,0,0,0,0,0,0,0,0,0,0,0,0.111111,0,0,0,0.2,0,0,0,0,0,0.2,0.125,0,0,0.285714,0,0.333333,0,0.4,0,0,0,0.333333,0,0.142857,0,0,0,0,0,0,0,0,0,0,0.125,0,0,0.111111,0,0.333333,1,0,0,0.125,0.285714,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.1,0,0,0,0,0,0,0,0.285714,0,0,0,0.5,1,0,0,0.25,0.2,0,0,0.222222,0,0]
precision
0.061186 [0,0,0,0.166667,0,0,0,0,1,0,0,0,0,0,0,0.111111,0,0,0,0,0,0,0,0,0,0.333333,0.142857,0,0.142857,0.4,0,0.333333,0,0.2,0,0,0,0,0,0,0,0.666667,0,0,0,0,0,0,0,0,0.166667,0,0,0,0,0,1,0,0,0.2,0.2,0.25,0,0.25,0,0,0,0,0,0,0,0,0,0.142857,0,0,0,0.333333,0,0,0,0,0,0,0,0.166667,0,0,0,0,0,0,0,0,0,0,0,0.285714,0,0]
precision
0.083961 [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.090909,0.333333,0,0,0,0,0,0,0,0,0.4,0.4,0,0.333333,0.4,0,0.5,0,0.111111,0,0,0,0,0,0.25,0,0,0.25,0,0,0,0,0,0,0,0.181818,0,0,0.090909,0,0.333333,1,0,0,0.142857,0.25,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0.181818,0,0,0,0.333333,0,0,0,0.666667,0,0,0,0.25,0,0]
precision
0.103197 [0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0.25,0,0.5,0,0.285714,0,0,0,0.2,0,0.333333,0,1,0,0,0,0,0,0,0,0,0.333333,0,0,0.090909,0,0,1,0,0,0.25,0.222222,0.333333,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0.285714,0,0,0,0,0,0,0,0.083333,0,0,0,0.4,1,0,0,0.4,0.333333,0,0,0.333333,0,0]
precision
0.06316 [0,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0.333333,0,0,0,0,0,0,0,0,0,0.111111,0,0,0,0.333333,0,0.4,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0.083333,0,0,0,0,0,0.666667,0,0,0.25,0.4,0.25,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0.333333,0,0,0,0,0,0,0,0,0,0,0,0.125,1,0,0,0.333333,0,0,0,0.222222,0,0]
precision
0.099638 [0,0,0,0.2,0,0,0,0,0,0,0,0,0,0.166667,0,0.25,0,0,0,0,0,0,0.5,0,0,0.142857,0.1,0,0,0.333333,0,0.222222,0,0.142857,0,0,0,0,0,0.285714,0,1,0,0,0,0,0,0,0,0,0.333333,0,0,0.142857,0,0.25,0.666667,0,0,0.666667,0.166667,0.285714,0,0.5,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0.5,0,0,0,0,0,0,0,0.1,0,0,0,0.25,0.5,0,0,0.666667,0,0,0,0.2,0,0]
precision
0.079253 [0,0,0,0.25,0,0,0,0,0,0,0,0,0,0.2,0,0.2,0,0,0,0,0,0,0,0,0,0.142857,0.2,0,0.25,0.333333,0,0.666667,0,0,0,0,0,0.111111,0,0.083333,0,1,0,0,0,0,0,0,0,0,0.125,0,0,0.111111,0,1,0.666667,0,0,0.5,0.125,0.166667,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0.142857,0,0,0,0,0,0,0,0.142857,0,0,0,0.285714,0,0,0,0.4,0,0,0,0.2,0,0]
predictive_accuracy
0.20625
predictive_accuracy
0.15
predictive_accuracy
0.19375
predictive_accuracy
0.1875
predictive_accuracy
0.15625
predictive_accuracy
0.20625
predictive_accuracy
0.2125
predictive_accuracy
0.15625
predictive_accuracy
0.225
predictive_accuracy
0.18239
prior_entropy
6.643831
prior_entropy
6.643831
prior_entropy
6.643831
prior_entropy
6.643831
prior_entropy
6.643831
prior_entropy
6.643831
prior_entropy
6.643831
prior_entropy
6.643831
prior_entropy
6.643831
prior_entropy
6.643831
recall
0.20625 [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0.5,0,0,1,1,0,1,0,0,1,0,0,0,0,0,0,0,0.5,0,1,0,0,0,0,0,0,0,0,1,0,0,0.5,0,0.5,1,0,0,1,1,0.5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0.5,0.5,0,0,1,0.5,0,0,1,0,0]
recall
0.15 [0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0.5,0,0,0,0,0,0,0,0,0.5,0.5,0,1,0,1,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0]
recall
0.19375 [0,0,0,0.5,0,0,0,0,0,0,0,0,0,0.5,0,1,1,0,0,0,0,0,0,0,0,1,1,0,1,1,0,1,0,0.5,0,0,0,0.5,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.5,0,0,0,1,1,0,0,1,0,0,0,1,0,0]
recall
0.1875 [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,1,0,0,0,0,0,0.5,1,0,0,1,0,1,0,1,0,0,0,0.5,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0.5,0,0.5,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,1,0,0,1,0,0]
recall
0.15625 [0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0.5,0.5,0,0.5,1,0,1,0,0.5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,1,0,0,0.5,1,1,0,0.5,0,0,0,0,0,0,0,0,0,0.5,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
recall
0.20625 [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0.5,0,0,0,0,0,0,0,0,1,1,0,1,1,0,1,0,0.5,0,0,0,0,0,1,0,0,0.5,0,0,0,0,0,0,0,1,0,0,1,0,1,0.5,0,0,0.5,1,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0]
recall
0.2125 [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,1,0,1,0,1,0,0,0,0.5,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0.5,1,0.5,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0.5,0,0,1,1,0,0,1,0,0]
recall
0.15625 [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,1,0,0,0.5,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0,0,0.5,0,0,0,1,0,0]
recall
0.225 [0,0,0,0.5,0,0,0,0,0,0,0,0,0,1,0,0.5,0,0,0,0,0,0,1,0,0,1,0.5,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0.5,0,0,0,0,0,0,0,0,1,0,0,0.5,0,1,1,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0.5,0.5,0,0,1,0,0,0,1,0,0]
recall
0.18239 [0,0,0,0.5,0,0,0,0,0,0,0,0,0,1,0,0.5,0,0,0,0,0,0,0,0,0,1,0.5,0,1,1,0,1,0,0,0,0,0,1,0,0.5,0,0.5,0,0,0,0,0,0,0,0,0.5,0,0,0.5,0,1,1,0,0,1,1,0.5,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0]
relative_absolute_error
0.801766
relative_absolute_error
0.858585
relative_absolute_error
0.814393
relative_absolute_error
0.820706
relative_absolute_error
0.852271
relative_absolute_error
0.801769
relative_absolute_error
0.795456
relative_absolute_error
0.852275
relative_absolute_error
0.78283
relative_absolute_error
0.825871
root_mean_prior_squared_error
0.099499
root_mean_prior_squared_error
0.099499
root_mean_prior_squared_error
0.099499
root_mean_prior_squared_error
0.099499
root_mean_prior_squared_error
0.099499
root_mean_prior_squared_error
0.099499
root_mean_prior_squared_error
0.099499
root_mean_prior_squared_error
0.099499
root_mean_prior_squared_error
0.099499
root_mean_prior_squared_error
0.099499
root_mean_squared_error
0.125996
root_mean_squared_error
0.130384
root_mean_squared_error
0.126984
root_mean_squared_error
0.127475
root_mean_squared_error
0.129904
root_mean_squared_error
0.125996
root_mean_squared_error
0.125499
root_mean_squared_error
0.129904
root_mean_squared_error
0.124499
root_mean_squared_error
0.127876
root_relative_squared_error
1.266306
root_relative_squared_error
1.310407
root_relative_squared_error
1.276238
root_relative_squared_error
1.281175
root_relative_squared_error
1.30558
root_relative_squared_error
1.26631
root_relative_squared_error
1.261315
root_relative_squared_error
1.305585
root_relative_squared_error
1.251265
root_relative_squared_error
1.285202
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0