10192641
1
Jan van Rijn
9954
Supervised Classification
9666
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(4)
8118103
copy
true
9559
fill_value
-1
9559
missing_values
NaN
9559
strategy
"constant"
9559
verbose
0
9559
n_jobs
null
9606
remainder
"passthrough"
9606
sparse_threshold
0.3
9606
transformer_weights
null
9606
transformers
[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}]
9606
memory
null
9607
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
9607
axis
0
9608
copy
true
9608
missing_values
"NaN"
9608
strategy
"median"
9608
verbose
0
9608
copy
true
9609
with_mean
true
9609
with_std
true
9609
memory
null
9610
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
9610
categorical_features
null
9611
categories
null
9611
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
9611
handle_unknown
"ignore"
9611
n_values
null
9611
sparse
true
9611
threshold
0.0
9612
memory
null
9666
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}]
9666
criterion
"friedman_mse"
9667
init
null
9667
learning_rate
0.014923214378349928
9667
loss
"deviance"
9667
max_depth
24
9667
max_features
0.8530041737394063
9667
max_leaf_nodes
null
9667
min_impurity_decrease
0.8308636904094762
9667
min_impurity_split
null
9667
min_samples_leaf
9
9667
min_samples_split
4
9667
min_weight_fraction_leaf
0.023417335504366033
9667
n_estimators
212
9667
n_iter_no_change
1764
9667
presort
"auto"
9667
random_state
55973
9667
subsample
0.5025335828060278
9667
tol
0.021176255760676613
9667
validation_fraction
0.6474944124396005
9667
verbose
0
9667
warm_start
false
9667
openml-python
Sklearn_0.20.3.
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
21306198
description
https://api.openml.org/data/download/21306198/description.xml
-1
21306199
predictions
https://api.openml.org/data/download/21306199/predictions.arff
area_under_roc_curve
0.5523800505050506 [0.73181,0.533183,0.445747,0.315538,0.400568,0.650016,0.244831,0.514362,0.470881,0.355903,0.42152,0.366359,0.770952,0.335464,0.248856,0.730271,0.543008,0.697838,0.533302,0.67006,0.838818,0.298098,0.459951,0.873501,0.486703,0.726602,0.615017,0.814216,0.477628,0.589883,0.463542,0.334596,0.806246,0.278212,0.479206,0.292337,0.4302,0.581795,0.193379,0.270005,0.708807,0.567945,0.171283,0.724215,0.193024,0.796461,0.100142,0.280974,0.906349,0.751006,0.772668,0.395833,0.552971,0.623501,0.622199,0.52616,0.553898,0.805319,0.421441,0.679589,0.400489,0.718099,0.118371,0.455966,0.234099,0.678662,0.777245,0.169034,0.549558,0.689591,0.628314,0.077415,0.570194,0.553859,0.505287,0.466027,0.5305,0.711667,0.248895,0.991832,0.787011,0.990688,0.733862,0.947404,0.885811,0.759746,0.992148,0.721334,0.770537,0.647806,0.58724,0.064512,0.313644,0.51523,0.661301,0.538865,0.707071,0.583649,0.654317,0.856534]
average_cost
0
kappa
0.041035353535353536
kb_relative_information_score
87.98191170033157
mean_absolute_error
0.019620762540871667
mean_prior_absolute_error
0.019800000000000036
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]
predictive_accuracy
0.050625
prior_entropy
6.6438561897747395
recall
0.050625 [0,0,0,0,0,0.0625,0,0,0,0,0,0,0.0625,0,0,0,0.125,0,0,0.0625,0.125,0,0,0.0625,0,0,0,0,0,0,0.0625,0,0.125,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0.0625,0,0,0,0,0,0,0,0.125,0,0,0,0,0,0,0,0,0,0,0,0,0,0.875,0,0.8125,0,0.75,0.375,0,0.6875,0,0,0,0,0,0,0,0,0,0,0,0,0.4375]
relative_absolute_error
0.9909476030743248
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.10004959448071439
root_relative_squared_error
1.0055362585446723
total_cost
0
area_under_roc_curve
0.55619427195287 [0.779874,0.642405,0.363924,0.213836,0.506329,0.194969,0.186709,0.100629,0.275316,0.91195,0.493671,0.275316,0.851266,0.202532,0.21519,0.761006,0.857595,0.768987,0.389241,0.918239,0.773585,0.157233,0.459119,0.993711,0.553459,0.563291,0.642405,0.857595,0.53481,0.666667,0.291139,0.427215,0.917722,0.265823,0.471519,0.138365,0.169811,0.383648,0,0.316456,0.899371,0.761006,0.091772,0.68038,0.072785,0.848101,0.301887,0.125786,0.974684,0.889241,0.898734,0.256329,0.238994,0.572785,0.704403,0.377358,0.949367,0.943396,0.062893,0.857595,0.352201,0.459119,0.072785,0.610759,0.427673,0.636076,0.886076,0.006289,0.792453,0.924528,0.731013,0.044025,0.490506,0.648734,0.377358,0.487342,0.379747,0.579114,0.297468,0.987342,0.518987,1,0.987421,0.981132,0.756329,0.75,0.990506,0.674051,0.693038,0.893082,0.544304,0.037736,0.212025,0.386076,0.75,0.509494,0.559748,0.636076,0.655063,0.974843]
area_under_roc_curve
0.5390565291378073 [0.962264,0.610759,0.332278,0.383648,0.167722,0.163522,0.167722,0.264151,0.560127,0.339623,0.598101,0.335443,0.724684,0.550633,0.101266,0.924528,0.370253,0.420886,0.462025,0.660377,0.962264,0.622642,0.572327,0.981132,0.622642,0.898734,0.648734,0.734177,0.417722,0.522013,0.398734,0.25,0.93038,0.155063,0.449367,0.201258,0.308176,0.949686,0.069182,0.234177,0.883648,0.666667,0.06962,0.617089,0.091772,0.871835,0.132075,0.037736,0.939873,0.848101,0.987342,0.278481,0.45283,0.389241,0.157233,0.150943,0.503165,0.90566,0.433962,0.742089,0.27673,0.842767,0.094937,0.553797,0.050314,0.696203,0.822785,0.062893,0.798742,0.830189,0.617089,0.012579,0.544304,0.696203,0.333333,0.170886,0.56962,0.78481,0.139241,0.987342,0.591772,0.977848,1,0.389937,0.778481,0.579114,1,0.531646,0.856013,0.566038,0.626582,0,0.496835,0.727848,0.753165,0.253165,0.779874,0.829114,0.762658,0.377358]
area_under_roc_curve
0.5735087771674229 [0.838608,0.572785,0.525316,0.301887,0.582278,0.572785,0.685535,0.943038,0.436709,0.287975,0.401899,0.370253,0.786164,0.306962,0.212025,0.661392,0.683544,0.582278,0.761006,0.509494,0.949686,0.358491,0.376582,0.993711,0.540881,0.601266,0.765823,0.943396,0.481013,0.553797,0.396226,0.25,0.756329,0.411392,0.119497,0.163522,0.572327,0.358491,0.320755,0.39557,0.588608,0.754717,0.08805,0.849057,0.439873,0.943396,0.031646,0.672956,0.85443,0.68038,0.737342,0.415094,0.591195,0.591772,0.345912,0.329114,0.955975,0.863924,0.855346,0.35443,0.459119,0.779874,0.03481,0.427673,0.306962,0.946203,0.860759,0.006329,0.477987,0.636076,0.56962,0.066456,0.18239,0.575949,0.710692,0.424051,0.933544,0.544304,0.21519,0.996835,0.798742,1,0.702532,0.987342,1,0.735849,1,0.791139,0.892405,0.81761,0.575949,0.069182,0.113208,0.506329,0.813291,0.591195,0.71519,0.528481,0.811321,0.851266]
area_under_roc_curve
0.552260842090598 [0.920886,0.623418,0.462025,0,0.234177,0.829114,0.408805,0.348101,0.484177,0.256329,0.550633,0.28481,0.735849,0.183544,0.329114,0.867089,0.528481,0.642405,0.830189,0.984177,0.238994,0.220126,0.620253,0.987421,0.194969,0.889241,0.262658,0.647799,0.452532,0.515823,0.327044,0.091772,0.955696,0.373418,0.389937,0.081761,0.559748,0.572327,0.735849,0.072785,0.677215,0.666667,0.194969,0.754717,0.189873,0.877358,0.139241,0.050314,0.996835,0.487342,0.582278,0.503145,0.371069,0.696203,0.72956,0.53481,0.006289,0.787975,0.402516,0.768987,0.446541,0.433962,0.06962,0.679245,0.351266,0.901899,0.718354,0.281646,0.465409,0.734177,0.781646,0.056962,0.886792,0.433544,0.540881,0.493671,0.683544,0.707278,0.113924,1,0.937107,1,0.813291,1,1,0.949686,1,0.816456,0.607595,0.54717,0.357595,0.006289,0.685535,0.436709,0.806962,0.45283,0.560127,0.620253,0.327044,0.787975]
area_under_roc_curve
0.5717982495422339 [0.677215,0.352201,0.537975,0.598101,0.257862,0.696203,0.113208,0.670886,0.666667,0.294304,0.43038,0.341772,0.685535,0.993711,0.157233,0.731013,0.693038,0.762658,0.339623,0.661392,0.971519,0.389241,0.335443,0.971519,0.575949,0.917722,0.541139,0.924528,0.427673,0.753165,0.446541,0.339623,0.950949,0.386076,0.553459,0.332278,0.620253,0.693038,0.107595,0.46519,0.797468,0.275316,0.113208,0.779874,0.031447,0.852201,0.085443,0.189873,0.908228,0.708861,0.672956,0.194969,0.471519,0.54717,0.503165,0.332278,0,0.724684,0.306962,0.685535,0.449367,0.702532,0.069182,0.289308,0.256329,0.708861,0.710692,0.03481,0.737342,0.849684,0.522152,0.075949,0.503145,0.742138,0.401899,0.937107,0.68038,0.767405,0.433544,1,0.943396,1,0.35443,0.993671,0.933962,0.993711,0.968553,0.971698,0.743671,0.737342,0.849057,0.053797,0.075472,0.761006,0.756329,0.754717,0.857595,0.339623,0.962264,0.746835]
area_under_roc_curve
0.5335074834806145 [0.974684,0.490566,0.632911,0.088608,0.081761,0.838608,0.27673,0.401899,0.264151,0.408228,0.341772,0.575949,0.81761,0.540881,0.157233,0.844937,0.348101,0.721519,0.433962,0.623418,0.556962,0.253165,0.376582,0.683544,0.310127,0.787975,0.636076,0.886792,0.540881,0.591772,0.440252,0.144654,0.85443,0.275316,0.647799,0.278481,0.427215,0.585443,0.221519,0.199367,0.837025,0.651899,0.427673,0.877358,0.09434,0.861635,0.03481,0.268987,0.938291,0.803797,0.971698,0.440252,0.598101,0.578616,0.743671,0.639241,0,0.901899,0.240506,0.345912,0.348101,0.794304,0.106918,0.314465,0.088608,0.332278,0.54717,0.031646,0.541139,0.528481,0.686709,0.110759,0.509434,0.213836,0.674051,0.641509,0.262658,0.889241,0.126582,1,0.899371,0.996835,0.648734,0.971519,1,0.823899,0.968553,0.798742,0.800633,0.620253,0.641509,0.136076,0.383648,0.18239,0.598101,0.553459,0.537975,0.245283,0.779874,0.993671]
area_under_roc_curve
0.5479580895231273 [0.870253,0.446541,0.72956,0.022152,0.119497,0.765823,0.246835,0.405063,0.339623,0.43038,0.163522,0.232704,0.81962,0.207547,0.075472,0.901899,0.226415,0.773585,0.490506,0.78481,0.943038,0.170886,0.531646,0.607595,0.607595,0.36478,0.679245,0.882911,0.345912,0.636076,0.566456,0.515723,0.761006,0.194969,0.481013,0.841772,0.446203,0.585443,0.341772,0.169811,0.594937,0.332278,0.050633,0.781646,0.597484,0.677215,0.186709,0.237342,0.955975,0.522013,0.389937,0.439873,0.582278,0.836478,0.493671,0.537975,0.496835,0.851266,0.224684,0.704403,0.313291,0.889241,0.044025,0.379747,0.278481,0.591195,0.798742,0.139241,0.803797,0.772152,0.471698,0.072785,0.439873,0.408805,0.28481,0.968553,0.062893,0.95283,0.056604,0.993711,0.954114,1,0.955696,0.990506,0.955696,0.734177,1,0.616352,0.742138,0.693038,0.622642,0.015823,0.370253,0.433962,0.786164,0.329114,0.806962,0.716981,0.518987,0.962025]
area_under_roc_curve
0.5713576496696121 [0.775316,0.54717,0.289308,0.550633,0.427673,0.927215,0.18038,0.677215,0.540881,0.329114,0.622642,0.320755,0.962025,0.194969,0.761006,0.544304,0.955975,0.830189,0.455696,0.607595,0.981013,0.151899,0.386076,0.879747,0.382911,0.874214,0.566038,0.60443,0.792453,0.560127,0.21519,0.421384,0.805031,0.18239,0.629747,0.21519,0.553797,0.53481,0.126582,0.144654,0.870253,0.699367,0.072785,0.740506,0.075472,0.825949,0.072785,0.420886,0.918239,0.971698,0.95283,0.528481,0.731013,0.836478,0.81962,0.699367,0.487342,0.759494,0.335443,0.704403,0.449367,0.903481,0.044025,0.253165,0.113924,0.622642,0.933962,0.525316,0.439873,0.439873,0.811321,0.06962,0.528481,0.465409,0.601266,0.138365,0.440252,0.36478,0.138365,0.993711,0.731013,1,0.64557,1,0.927215,0.844937,1,0.716981,0.91195,0.617089,0.767296,0.022152,0.373418,0.238994,0.786164,0.613924,0.693038,0.968553,0.810127,0.772152]
area_under_roc_curve
0.5501067291616906 [0.754717,0.550633,0.245283,0.094937,0.544304,0.528302,0.265823,0.132075,0.373418,0.106918,0.377358,0.264151,0.936709,0.205696,0.189873,0.735849,0.18239,0.72956,0.484177,0.616352,0.987342,0.287975,0.72956,0.781646,0.43038,0.874214,0.679245,0.835443,0.392405,0.264151,0.601266,0.21519,0.955975,0.207547,0.528481,0.186709,0.161392,0.496835,0.132911,0.477987,0.666667,0.471519,0.357595,0.753165,0.082278,0.81962,0.025157,0.329114,0.974843,0.955975,0.962025,0.420886,0.648734,0.693038,0.924051,0.874214,0.857595,0.773585,0.189873,0.746835,0.436709,0.64557,0.025316,0.392405,0.125786,0.937107,0.689873,0.54717,0.572785,0.886792,0.798742,0.201258,0.617089,0.338608,0.367089,0.60443,0.836478,0.672956,0.232704,0.996835,0.914557,0.993711,0.981132,0.981132,0.984177,0.658228,0.993671,0.636076,0.842767,0.594937,0.765823,0.047468,0.325949,0.648734,0.578616,0.800633,0.578616,0.259494,0.601266,0.974843]
area_under_roc_curve
0.5386646913064245 [0.496855,0.424051,0.232704,0.712025,0.420886,0.119497,0.10443,0.955975,0.427215,0.371069,0.113208,0.45283,0.458861,0.186709,0.085443,0.584906,0.314465,0.654088,0.740506,0.666667,0.759494,0.493671,0.308176,0.962025,0.658228,0.251572,0.679245,0.919304,0.427215,0.654088,0.841772,0.386076,0.955975,0.132075,0.344937,0.10443,0.493671,0.436709,0.091772,0.201258,0.622642,0.455696,0.313291,0.753165,0.158228,0.787975,0.056604,0.329114,0.981132,0.666667,0.881329,0.496835,0.707278,0.746835,0.56962,0.459119,0.613924,0.874214,0.490506,0.851266,0.446203,0.471519,0.370253,0.667722,0.27044,0.672956,0.734177,0.025157,0.329114,0.921384,0.27673,0.018868,0.598101,0.598101,0.636076,0.205696,0.081761,0.811321,0.169811,0.981013,0.802215,0.974843,0.773585,1,0.966772,0.955696,0.993671,0.765823,0.603774,0.585443,0.550633,0.056962,0.123418,0.620253,0.383648,0.541139,0.798742,0.610759,0.56962,0.968553]
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
kappa
0.04044190175577037
kappa
0.03473837782421828
kappa
0.05930631732628339
kappa
0.06595140422846323
kappa
0.05284344291408501
kappa
0.03359519955785401
kappa
0.04025256511444357
kappa
0.03998736825484546
kappa
0.021426034802509566
kappa
0.021001105321332703
kb_relative_information_score
9.710578498729122
kb_relative_information_score
7.987211475383797
kb_relative_information_score
9.877224693914567
kb_relative_information_score
10.508852260343339
kb_relative_information_score
10.6398117964142
kb_relative_information_score
6.925699366877361
kb_relative_information_score
9.258461154803927
kb_relative_information_score
8.739012196517974
kb_relative_information_score
7.834345814444383
kb_relative_information_score
6.500714442902929
mean_absolute_error
0.01957447378833311
mean_absolute_error
0.019651512724546585
mean_absolute_error
0.01959656749357195
mean_absolute_error
0.01952019997642982
mean_absolute_error
0.019571512059614136
mean_absolute_error
0.01964256784592811
mean_absolute_error
0.019625531570847284
mean_absolute_error
0.019634631392105778
mean_absolute_error
0.019661528847779396
mean_absolute_error
0.019729099709560577
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
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]
predictive_accuracy
0.05
predictive_accuracy
0.04375
predictive_accuracy
0.06875
predictive_accuracy
0.075
predictive_accuracy
0.0625
predictive_accuracy
0.04375
predictive_accuracy
0.05
predictive_accuracy
0.05
predictive_accuracy
0.03125
predictive_accuracy
0.03125
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
recall
0.05 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,1,0,0,0.5,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0]
recall
0.04375 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0.5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]
recall
0.06875 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.5]
recall
0.075 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.5]
recall
0.0625 [0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5]
recall
0.04375 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0.5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]
recall
0.05 [0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.5]
recall
0.05 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]
recall
0.03125 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0]
recall
0.03125 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0.5,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,1]
relative_absolute_error
0.9886097872895493
relative_absolute_error
0.9925006426538663
relative_absolute_error
0.9897256309884807
relative_absolute_error
0.9858686856782722
relative_absolute_error
0.9884602050310153
relative_absolute_error
0.9920488811074786
relative_absolute_error
0.9911884631741036
relative_absolute_error
0.9916480501063508
relative_absolute_error
0.9930065074636043
relative_absolute_error
0.9964191772505326
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_squared_error
0.10012592843203662
root_mean_squared_error
0.10015785261013646
root_mean_squared_error
0.09975977253589344
root_mean_squared_error
0.09956070673638473
root_mean_squared_error
0.09989247533033185
root_mean_squared_error
0.10026213588004589
root_mean_squared_error
0.10020496025364257
root_mean_squared_error
0.1000337950584266
root_mean_squared_error
0.09990411469046048
root_mean_squared_error
0.10059046260596197
root_relative_squared_error
1.0063034436213423
root_relative_squared_error
1.0066242936834566
root_relative_squared_error
1.0026234384023023
root_relative_squared_error
1.0006227518399917
root_relative_squared_error
1.0039571516683141
root_relative_squared_error
1.0076723799810352
root_relative_squared_error
1.0070977433145705
root_relative_squared_error
1.0053774683760879
root_relative_squared_error
1.004074131639061
root_relative_squared_error
1.0109721877340943
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
usercpu_time_millis
22757.589312001073
usercpu_time_millis
22679.27983100526
usercpu_time_millis
22647.68039500632
usercpu_time_millis
22769.8374689935
usercpu_time_millis
22681.611562999024
usercpu_time_millis
22734.88137000095
usercpu_time_millis
22647.70326099824
usercpu_time_millis
22762.08708399645
usercpu_time_millis
22639.722829990205
usercpu_time_millis
22692.120497005817
usercpu_time_millis_testing
12.937448002048768
usercpu_time_millis_testing
12.071575001755264
usercpu_time_millis_testing
14.377072002389468
usercpu_time_millis_testing
12.312351995205972
usercpu_time_millis_testing
16.103449997899588
usercpu_time_millis_testing
12.514809001004323
usercpu_time_millis_testing
12.970176001545042
usercpu_time_millis_testing
12.463958999433089
usercpu_time_millis_testing
12.867071993241552
usercpu_time_millis_testing
12.46635600546142
usercpu_time_millis_training
22744.651863999024
usercpu_time_millis_training
22667.208256003505
usercpu_time_millis_training
22633.30332300393
usercpu_time_millis_training
22757.525116998295
usercpu_time_millis_training
22665.508113001124
usercpu_time_millis_training
22722.366560999944
usercpu_time_millis_training
22634.733084996697
usercpu_time_millis_training
22749.623124997015
usercpu_time_millis_training
22626.855757996964
usercpu_time_millis_training
22679.654141000356