8881248
710
William Raynaut
9954
Supervised Classification
7838
weka.kf.AdaBoostM1-NaiveBayes(1)
6833449
mDataExp
Modelage
study_107
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
18678249
description
https://api.openml.org/data/download/18678249/description1832523914178486956.xml
-1
18678250
predictions
https://api.openml.org/data/download/18678250/predictions1682286995213231081.arff
area_under_roc_curve
0.939527 [0.900351,0.934659,1,1,0.96733,0.966166,0.96733,0.999684,0.934304,0.965002,0.966619,1,0.96733,0.901712,1,0.999625,0.995857,0.733112,0.834774,0.793403,0.93462,1,0.901298,0.96733,0.966639,0.832505,0.927123,0.999684,0.96733,0.934659,1,0.999684,0.96733,0.8285,1,0.932726,0.930812,0.863518,0.934343,0.934659,0.899582,0.999684,0.96733,0.999369,0.999684,1,0.934028,0.96656,0.998737,0.90191,0.932528,0.835701,0.934028,0.932548,0.868608,0.896149,1,0.934659,0.89826,0.866221,0.999369,0.999369,0.803464,0.967014,0.998836,0.963995,0.896445,0.96733,0.9666,0.835069,0.96729,0.900805,0.934028,1,0.900253,0.966698,0.965002,0.802557,0.999369,0.934265,0.933041,0.93168,0.934699,0.866576,0.933988,0.932884,0.999684,0.901989,0.966383,0.999684,0.932884,0.96733,0.901357,0.901042,0.964134,0.967014,0.933396,0.966698,0.834852,1]
average_cost
0
f_measure
0.845245 [0.742857,0.933333,1,1,0.967742,0.903226,0.967742,0.969697,0.875,0.933333,0.909091,1,0.967742,0.758621,1,0.941176,0.933333,0.357143,0.571429,0.285714,0.933333,1,0.8125,0.967742,0.909091,0.466667,0.484848,0.969697,0.967742,0.933333,1,0.969697,0.967742,0.466667,0.969697,0.787879,0.6875,0.551724,0.903226,0.933333,0.685714,0.941176,0.967742,0.941176,0.969697,1,0.875,0.882353,0.888889,0.83871,0.83871,0.733333,0.875,0.685714,0.8,0.5,1,0.933333,0.709677,0.62069,0.914286,0.941176,0.666667,0.9375,0.9375,0.848485,0.666667,0.967742,0.875,0.666667,0.933333,0.8125,0.83871,1,0.764706,0.909091,0.933333,0.6,0.941176,0.848485,0.777778,0.8125,0.896552,0.647059,0.875,0.866667,0.969697,0.896552,0.857143,0.969697,0.896552,0.967742,0.83871,0.8125,0.848485,0.9375,0.823529,0.909091,0.5625,0.969697]
kappa
0.842803
kb_relative_information_score
1356.430771
mean_absolute_error
0.003118
mean_prior_absolute_error
0.0198
number_of_instances
1600 [16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16]
precision
0.851409 [0.684211,1,1,1,1,0.933333,1,0.941176,0.875,1,0.882353,1,1,0.846154,1,0.888889,1,0.416667,0.526316,0.230769,1,1,0.8125,1,0.882353,0.5,0.470588,0.941176,1,1,1,0.941176,1,0.5,0.941176,0.764706,0.6875,0.615385,0.933333,1,0.631579,0.888889,1,0.888889,0.941176,1,0.875,0.833333,0.8,0.866667,0.866667,0.785714,0.875,0.631579,0.857143,0.5,1,1,0.733333,0.692308,0.842105,0.888889,0.818182,0.9375,0.9375,0.823529,0.647059,1,0.875,0.647059,1,0.8125,0.866667,1,0.722222,0.882353,1,0.642857,0.888889,0.823529,0.7,0.8125,1,0.611111,0.875,0.928571,0.941176,1,0.789474,0.941176,1,1,0.866667,0.8125,0.823529,0.9375,0.777778,0.882353,0.5625,0.941176]
predictive_accuracy
0.844375
prior_entropy
6.643856
recall
0.844375 [0.8125,0.875,1,1,0.9375,0.875,0.9375,1,0.875,0.875,0.9375,1,0.9375,0.6875,1,1,0.875,0.3125,0.625,0.375,0.875,1,0.8125,0.9375,0.9375,0.4375,0.5,1,0.9375,0.875,1,1,0.9375,0.4375,1,0.8125,0.6875,0.5,0.875,0.875,0.75,1,0.9375,1,1,1,0.875,0.9375,1,0.8125,0.8125,0.6875,0.875,0.75,0.75,0.5,1,0.875,0.6875,0.5625,1,1,0.5625,0.9375,0.9375,0.875,0.6875,0.9375,0.875,0.6875,0.875,0.8125,0.8125,1,0.8125,0.9375,0.875,0.5625,1,0.875,0.875,0.8125,0.8125,0.6875,0.875,0.8125,1,0.8125,0.9375,1,0.8125,0.9375,0.8125,0.8125,0.875,0.9375,0.875,0.9375,0.5625,1]
relative_absolute_error
0.157491
root_mean_prior_squared_error
0.099499
root_mean_squared_error
0.055362
root_relative_squared_error
0.556407
total_cost
0
area_under_roc_curve
0.909952 [0.996855,0.745253,1,1,1,0.996855,1,0.996855,0.745253,1,0.745253,1,1,0.490506,1,1,1,0.487342,0.740506,0.996855,1,1,1,1,0.490566,0.993671,0.992089,0.996835,0.745253,1,1,1,1,0.490506,1,0.996855,1,0.993711,1,1,0.996855,1,0.745253,0.996835,1,1,1,0.993711,1,0.745253,1,0.745253,0.996855,0.996835,0.487421,1,1,1,1,1,1,1,0.490506,1,1,1,1,1,1,0.487421,1,0.996855,1,1,0.487421,1,0.745253,0.490506,1,1,1,1,1,0.996855,1,0.745253,1,1,0.745253,1,0.745253,1,1,0.996835,1,1,0.490566,0.993671,0.740506,1]
area_under_roc_curve
0.945552 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.746835,1,1,1,0.746835,0.746835,0.484277,1,1,1,1,1,0.743671,0.995253,1,1,1,1,1,1,1,1,0.996855,0.993711,1,1,0.746835,1,1,1,1,1,1,1,1,0.996835,0.746835,1,0.746835,1,0.996835,1,0.490566,1,0.493711,1,0.743671,1,0.996855,0.746835,0.996835,1,0.996835,1,1,0.996855,0.996855,1,1,1,1,1,0.996835,0.996835,1,1,0.996835,0.746835,1,1,0.493711,0.743671,1,1,1,1,1,0.746835,1,1,1,1,1,1,1,0.746835,1]
area_under_roc_curve
0.945649 [0.731013,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.468354,1,0.716772,1,1,0.468354,1,1,0.96519,1,1,1,1,1,1,0.734177,0.72943,1,1,0.996855,1,1,1,0.731013,1,1,1,0.996835,1,0.734177,1,1,1,1,1,1,1,1,0.734177,1,1,0.996855,0.734177,1,1,1,1,1,0.996835,1,1,1,0.734177,1,1,1,1,0.993711,1,1,0.995253,0.996835,0.732595,0.996855,1,1,0.732595,1,1,1,1,0.996835,1,1,1,1,0.734177,1,1,0.996835,1,1,1]
area_under_roc_curve
0.950897 [0.996835,1,1,1,0.72943,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.971519,0.459119,0.955696,1,1,1,1,1,1,1,1,1,1,1,1,1,0.727848,1,1,1,1,1,0.72943,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.726266,1,0.996835,1,1,1,1,0.993711,1,0.998418,1,1,0.969937,0.724684,1,1,0.72943,0.72943,0.727848,0.459119,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.72943,1,1,1,1,0.459119,0.996835,0.996835,0.459119,0.72943,1,0.993711,1]
area_under_roc_curve
0.928568 [1,0.471698,1,1,1,1,1,1,0.471698,0.977848,0.996835,1,1,1,1,1,0.982595,0.734177,1,0.726266,0.471519,1,1,1,1,1,0.727848,1,1,0.735759,1,1,1,0.708861,1,1,1,0.707278,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.471698,0.468553,0.735759,0.996855,0.996835,0.984177,1,1,1,1,1,1,1,1,1,1,0.468553,1,1,0.732595,1,1,1,1,0.735759,1,1,0.735759,1,1,0.996855,1,0.737342,0.996835,0.996855,1,1,1,1,0.996835,1,0.735759,0.996855,0.471698,0.735759,1,0.996835,1,0.993711,1]
area_under_roc_curve
0.947671 [0.727848,1,1,1,1,0.731013,1,1,1,0.731013,0.996835,1,0.462264,1,1,1,0.966772,1,1,0.981013,1,1,1,1,0.996835,0.732595,0.987342,1,1,1,1,0.996855,1,0.996835,1,0.985759,0.996835,0.702532,1,1,0.996835,0.996835,1,0.996855,1,1,1,1,1,0.731013,1,0.996855,0.727848,1,0.731013,1,1,1,1,1,1,1,1,1,1,1,1,0.731013,1,0.731013,1,0.996835,1,1,1,1,1,0.731013,1,1,1,0.72943,1,1,1,1,1,1,0.996835,1,1,1,0.462264,1,1,0.996855,0.996835,0.462264,0.993711,1]
area_under_roc_curve
0.936315 [0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.490566,0.745253,0.740506,1,1,0.993671,1,0.996835,0.493711,0.496855,1,1,1,1,1,1,0.993711,1,1,0.743671,0.746835,0.996835,1,0.745253,1,1,1,1,1,0.748418,1,1,1,1,0.996835,1,1,0.748418,0.745253,1,1,0.496835,0.996855,1,1,1,0.748418,1,1,1,1,1,1,1,1,1,1,1,0.996855,1,0.493711,1,1,0.748418,1,1,0.993671,1,1,1,0.496855,1,1,1,1,0.748418,1,0.996855,1,1,1,0.496835,1]
area_under_roc_curve
0.955528 [0.735759,1,1,1,1,1,0.735759,1,1,1,1,1,1,1,1,1,1,0.465409,0.993671,0.996835,1,1,1,1,1,0.471698,1,1,1,0.735759,1,1,1,0.955975,1,1,0.737342,1,1,1,1,1,1,1,1,1,1,1,0.996855,1,0.468553,0.735759,1,1,1,0.984177,1,1,0.996835,0.471698,1,0.996835,1,1,1,0.468553,0.946541,1,1,0.996835,1,1,1,1,0.996835,1,1,1,1,1,0.993671,1,0.735759,0.735759,0.735759,0.981013,1,1,1,1,1,1,0.996835,0.996855,1,1,1,1,1,1]
area_under_roc_curve
0.940022 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,1,0.481132,1,1,0.477987,0.740506,1,1,0.993711,1,1,1,1,1,1,0.981132,1,1,1,0.738924,0.740506,1,0.481132,1,1,1,1,1,0.996855,1,0.996855,1,0.988924,0.740506,1,0.737342,0.740506,0.987421,1,1,0.737342,0.740506,1,1,0.481013,1,1,1,1,1,0.996835,0.996855,1,0.477987,0.993671,1,1,1,1,0.474843,0.996855,0.738924,0.996835,0.965409,1,0.993711,1,1,1,0.740506,0.996855,1,0.987342,1,1,1,0.959119,1,1,1,0.738924,1]
area_under_roc_curve
0.937014 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.737342,0.468553,1,1,1,1,1,0.474843,0.968553,1,1,1,1,1,1,0.990566,1,0.477848,1,1,0.738924,1,1,1,1,1,1,1,0.996855,0.738924,1,1,1,1,1,1,1,1,1,0.477987,0.984177,0.992089,1,1,0.738924,1,0.996855,1,0.738924,1,0.737342,1,1,0.477987,0.735759,1,0.732595,0.738924,1,1,1,1,1,0.474843,1,0.471698,1,0.735759,0.996835,1,1,1,1,1,1,0.738924,1,1,0.996855,1,1,1]
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
0.776875 [0.666667,0.666667,1,1,1,0.666667,1,0.666667,0.666667,1,0.666667,1,1,0,1,1,1,0,0.5,0.666667,1,1,1,1,0,0.5,0.5,0.8,0.666667,1,1,1,1,0,1,0.666667,1,0.666667,1,1,0.666667,1,0.666667,0.8,1,1,1,0.5,1,0.666667,1,0.666667,0.666667,0.8,0,0,1,1,0,0.8,1,1,0,1,1,1,0.666667,1,1,0,1,0.666667,1,1,0,1,0.666667,0,1,1,1,1,1,0.666667,1,0.666667,1,1,0.666667,1,0.666667,1,1,0.8,1,1,0,0.666667,0.4,1]
f_measure
0.869583 [0.666667,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,0.666667,0.666667,0,1,1,1,1,1,0.5,0.8,1,1,1,1,1,1,1,1,0.666667,0,1,1,0.666667,1,1,1,1,1,1,1,1,0.8,0.666667,0.8,0.666667,1,0.666667,1,0,1,0,1,0.5,1,0.666667,0.666667,0.8,1,0.8,1,1,0.666667,0.666667,1,1,1,1,1,0.8,0.666667,0.8,1,0.8,0.666667,1,1,0,0.5,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,0.666667,1]
f_measure
0.850208 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,0,0,0.333333,1,1,0,1,1,0.5,0.666667,1,1,1,1,1,0.666667,0.5,1,1,0.666667,0,1,1,0.5,0.666667,1,1,0.8,1,0.666667,1,1,1,1,1,1,0.8,1,0.666667,1,1,0.666667,0.666667,1,1,1,1,1,0.8,1,1,0,0.666667,1,1,1,1,0.666667,1,1,0.8,0.8,0.666667,0.666667,1,1,0.666667,1,1,1,1,0.8,1,1,1,1,0.666667,1,1,0.8,1,0.5,1]
f_measure
0.862083 [0.8,1,1,1,0.666667,1,1,1,1,1,1,1,1,1,1,0.8,1,0.5,0,0.4,1,1,1,1,1,0.666667,0.8,1,1,1,1,1,1,0.666667,1,0.666667,1,0.666667,1,0.666667,0.8,1,1,1,1,1,1,1,0.8,0.8,1,1,1,0.5,1,0.666667,1,1,1,0.666667,0.4,1,0.8,1,1,0.666667,0.5,1,1,0.666667,0.666667,0.666667,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,0,0.8,0.8,0,0.666667,1,0,1]
f_measure
0.778839 [1,0,1,1,1,1,1,1,0,0.666667,0.8,1,1,0.5,1,1,0.666667,0,0.4,0.285714,0,1,1,1,1,0,0,1,1,0.666667,1,1,1,0,1,1,0.666667,0,1,1,0.666667,1,1,1,1,1,1,1,1,1,0,0,0.666667,0.666667,0.8,0.571429,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,0.666667,1,1,0.666667,1,1,0.666667,0.8,0,0.666667,0.666667,1,1,1,1,0.8,1,0.666667,0.666667,0,0.666667,1,0.8,1,0.666667,1]
f_measure
0.834167 [0.5,1,1,1,1,0.666667,1,1,1,0.666667,0.8,1,0,1,1,1,0.666667,1,1,0,1,1,1,1,0.8,0.666667,0.333333,1,1,1,1,0.666667,1,0.5,1,0.666667,0.666667,0,1,1,0.8,0.8,1,0.666667,1,1,1,1,1,0.666667,1,0.666667,0.5,1,0.666667,0.8,1,1,1,1,1,1,1,1,1,1,0.666667,0.666667,1,0.666667,0.666667,0.8,1,1,1,1,1,0.666667,1,1,1,0.666667,1,1,1,1,1,1,0.666667,1,1,1,0,1,1,0.666667,0.8,0,0,0.8]
f_measure
0.8375 [0.8,1,1,1,1,0.666667,1,1,0.666667,1,1,1,1,1,1,1,1,0,0.4,0.4,1,1,0.666667,1,0.8,0,0,1,1,1,1,1,1,0.666667,1,1,0.5,0.666667,0.8,1,0.5,1,1,1,1,1,0.666667,1,1,1,1,0.8,1,0.666667,0.666667,0,1,1,0,0.666667,1,1,1,0.666667,0.666667,1,0.666667,1,1,1,1,1,1,1,1,0.666667,1,0,1,1,0.666667,1,1,0.666667,1,1,1,0,1,1,1,1,0.666667,1,0.666667,1,1,1,0,1]
f_measure
0.863333 [0.666667,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,1,1,1,0,0.666667,0.666667,1,1,1,1,1,0,1,1,1,0.666667,1,1,1,0,1,0.8,0,0.8,1,1,0.666667,1,1,1,1,1,1,1,0.666667,1,0,0.666667,1,1,1,0.666667,1,1,0.8,0,1,0.8,1,1,1,0,0,1,1,0.8,1,1,1,1,0.8,1,1,0.666667,1,1,0.666667,1,0.666667,0.666667,0.666667,0.666667,1,1,1,1,1,1,0.8,0.666667,1,1,1,1,0.8,1]
f_measure
0.81 [1,1,1,1,1,1,1,1,0.8,1,1,1,1,1,1,0.666667,1,0.5,1,0,1,1,0,0.666667,1,1,0,1,1,1,1,1,1,0,1,1,1,0,0.666667,1,0,1,1,1,1,1,0.666667,1,0.666667,1,0.666667,0.666667,1,0.4,0.666667,0,1,1,0.5,0,1,1,0,1,1,1,0.8,1,0.8,0.666667,1,0,0.5,1,1,1,1,0,0.666667,0.5,0.666667,0,1,0.5,1,1,1,0.666667,0.666667,1,0.666667,1,1,1,0,1,1,1,0.666667,1]
f_measure
0.822917 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,0,0.666667,0,1,1,1,1,1,0,0,1,1,1,1,1,1,0.4,0.8,0,1,1,0.666667,1,1,1,1,1,1,1,0.666667,0.5,1,0.666667,1,1,1,0.666667,1,0.666667,1,0,0.5,0.5,1,1,0,1,0.666667,1,0.666667,1,0.666667,0.666667,1,0,0.5,1,0.4,0.666667,1,0,1,0.8,1,0,1,0,1,0.5,0.8,1,1,1,1,1,1,0.666667,1,1,0.666667,1,1,1]
kappa
0.797852
kappa
0.873638
kappa
0.861001
kappa
0.867309
kappa
0.804154
kappa
0.842028
kappa
0.854683
kappa
0.873623
kappa
0.823121
kappa
0.829438
kb_relative_information_score
128.326136
kb_relative_information_score
139.956347
kb_relative_information_score
139.278675
kb_relative_information_score
140.306387
kb_relative_information_score
129.742135
kb_relative_information_score
135.616729
kb_relative_information_score
136.858233
kb_relative_information_score
140.773549
kb_relative_information_score
132.627427
kb_relative_information_score
132.945153
mean_absolute_error
0.003992
mean_absolute_error
0.0025
mean_absolute_error
0.002746
mean_absolute_error
0.002623
mean_absolute_error
0.003904
mean_absolute_error
0.003126
mean_absolute_error
0.002918
mean_absolute_error
0.002516
mean_absolute_error
0.003486
mean_absolute_error
0.003373
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 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1]
number_of_instances
160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1]
number_of_instances
160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2]
number_of_instances
160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2]
number_of_instances
160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2]
number_of_instances
160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2]
number_of_instances
160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2]
number_of_instances
160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2]
number_of_instances
160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1]
number_of_instances
160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1]
precision
0.802083 [0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0,1,1,1,0,0.5,0.5,1,1,1,1,0,0.333333,0.5,0.666667,1,1,1,1,1,0,1,0.5,1,0.5,1,1,0.5,1,1,0.666667,1,1,1,0.333333,1,1,1,1,0.5,0.666667,0,0,1,1,0,0.666667,1,1,0,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.666667,1,1,0,0.5,0.333333,1]
precision
0.901042 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.666667,1,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,0.666667,1,1,1,1,0,1,0,1,0.5,1,0.5,1,0.666667,1,0.666667,1,1,0.5,0.5,1,1,1,1,1,0.666667,1,0.666667,1,0.666667,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
precision
0.872917 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0.25,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,0.5,0.5,1,1,0.666667,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,0.5,1,1,1,1,1,1,0.666667,1,1,0,1,1,1,1,1,0.5,1,1,0.666667,0.666667,1,0.5,1,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,0.666667,1,0.333333,1]
precision
0.893229 [0.666667,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,0.5,0,0.333333,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0.666667,1,1,1,1,1,1,1,0.666667,0.666667,1,1,1,0.5,1,1,1,1,1,1,0.25,1,0.666667,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.666667,0.666667,0,1,1,0,1]
precision
0.793437 [1,0,1,1,1,1,1,1,0,1,0.666667,1,1,0.333333,1,1,1,0,0.25,0.2,0,1,1,1,1,0,0,1,1,1,1,1,1,0,1,1,0.5,0,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,0.5,0.666667,0.4,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.666667,0,1,0.5,1,1,1,1,0.666667,1,1,0.5,0,1,1,0.666667,1,0.5,1]
precision
0.866667 [0.5,1,1,1,1,1,1,1,1,1,0.666667,1,0,1,1,1,1,1,1,0,1,1,1,1,0.666667,1,0.25,1,1,1,1,0.5,1,0.5,1,1,1,0,1,1,0.666667,0.666667,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,0.666667,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.666667,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,0.666667,0,0,0.666667]
precision
0.851042 [0.666667,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.333333,0.333333,1,1,0.5,1,0.666667,0,0,1,1,1,1,1,1,0.5,1,1,0.5,1,0.666667,1,0.5,1,1,1,1,1,1,1,1,1,1,0.666667,1,0.5,1,0,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0,1]
precision
0.888542 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,0.666667,0,0.666667,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0.666667,0,1,0.666667,1,1,1,0,0,1,1,0.666667,1,1,1,1,0.666667,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.666667,0.5,1,1,1,1,0.666667,1]
precision
0.827083 [1,1,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,0.5,1,0.333333,1,0,1,1,0,1,1,1,0,1,1,1,1,1,1,0,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.333333,1,0,1,1,0.5,0,1,1,0,1,1,1,0.666667,1,0.666667,0.5,1,0,0.5,1,1,1,1,0,0.5,0.5,0.5,0,1,0.333333,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1]
precision
0.843229 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1,0.25,0.666667,0,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,0.5,1,0,0.5,0.5,1,1,0,1,0.5,1,1,1,1,0.5,1,0,0.5,1,0.333333,1,1,0,1,0.666667,1,0,1,0,1,0.5,0.666667,1,1,1,1,1,1,1,1,1,0.5,1,1,1]
predictive_accuracy
0.8
predictive_accuracy
0.875
predictive_accuracy
0.8625
predictive_accuracy
0.86875
predictive_accuracy
0.80625
predictive_accuracy
0.84375
predictive_accuracy
0.85625
predictive_accuracy
0.875
predictive_accuracy
0.825
predictive_accuracy
0.83125
prior_entropy
6.643856
prior_entropy
6.643856
prior_entropy
6.643856
prior_entropy
6.643856
prior_entropy
6.643856
prior_entropy
6.643856
prior_entropy
6.643856
prior_entropy
6.643856
prior_entropy
6.643856
prior_entropy
6.643856
recall
0.8 [1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0,0.5,1,1,1,1,1,0,1,0.5,1,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,0,0,1,1,0,1,1,1,0,1,1,1,0.5,1,1,0,1,1,1,1,0,1,0.5,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,0,1,0.5,1]
recall
0.875 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0.5,1,0,1,0,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1]
recall
0.8625 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0.5,1,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1]
recall
0.86875 [1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,0.5,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0,0.5,1,0,1]
recall
0.80625 [1,0,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,0,1,0.5,0,1,1,1,1,0,0,1,1,0.5,1,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,1,1]
recall
0.84375 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0,1,1,1,0.5,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,0,1]
recall
0.85625 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0.5,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.875 [0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
recall
0.825 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,0,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,0,1,0.5,1,0,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1,1,1,0.5,1]
recall
0.83125 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,1,1,0,0.5,1,0.5,0.5,1,0,1,1,1,0,1,0,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1]
relative_absolute_error
0.201631
relative_absolute_error
0.126263
relative_absolute_error
0.138676
relative_absolute_error
0.132458
relative_absolute_error
0.197169
relative_absolute_error
0.157868
relative_absolute_error
0.147375
relative_absolute_error
0.127057
relative_absolute_error
0.176077
relative_absolute_error
0.170337
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
0.063123
root_mean_squared_error
0.049953
root_mean_squared_error
0.051325
root_mean_squared_error
0.050403
root_mean_squared_error
0.061883
root_mean_squared_error
0.055016
root_mean_squared_error
0.053755
root_mean_squared_error
0.049614
root_mean_squared_error
0.058484
root_mean_squared_error
0.058026
root_relative_squared_error
0.634408
root_relative_squared_error
0.502045
root_relative_squared_error
0.515838
root_relative_squared_error
0.506567
root_relative_squared_error
0.621946
root_relative_squared_error
0.552928
root_relative_squared_error
0.540263
root_relative_squared_error
0.49864
root_relative_squared_error
0.587785
root_relative_squared_error
0.583182
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