10177040
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)
8102502
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
"most_frequent"
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
0.00017365035459504464
9667
loss
"deviance"
9667
max_depth
23
9667
max_features
0.66656553661313
9667
max_leaf_nodes
null
9667
min_impurity_decrease
0.08871299378588704
9667
min_impurity_split
null
9667
min_samples_leaf
2
9667
min_samples_split
8
9667
min_weight_fraction_leaf
0.46119113448843
9667
n_estimators
180
9667
n_iter_no_change
402
9667
presort
"auto"
9667
random_state
42587
9667
subsample
0.29509052907389055
9667
tol
4.394493626499898e-05
9667
validation_fraction
0.3154728749776492
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
21274996
description
https://api.openml.org/data/download/21274996/description.xml
-1
21274997
predictions
https://api.openml.org/data/download/21274997/predictions.arff
area_under_roc_curve
0.5876629577020204 [0.854522,0.30157,0.418008,0.805161,0.651397,0.775963,0.536182,0.895163,0.668008,0.71733,0.181187,0.389599,0.813526,0.47242,0.250908,0.64173,0.76093,0.317274,0.799953,0.683002,0.884509,0.203086,0.582741,0.889836,0.679451,0.893624,0.51527,0.75,0.406171,0.277304,0.873224,0.404356,0.839923,0.278804,0.107521,0.663984,0.590712,0.547072,0.631905,0.21366,0.070747,0.50146,0.644926,0.32047,0.602865,0.608744,0.277304,0.404277,0.647214,0.586253,0.780224,0.787287,0.480587,0.102943,0.500552,0.341698,0.8314,0.689433,0.320155,0.704782,0.420691,0.54652,0.327217,0.42444,0.403093,0.583491,0.53342,0.372396,0.628433,0.498935,0.394965,0.510496,0.697956,0.276081,0.666588,0.60101,0.525529,0.735519,0.443537,0.931937,0.90842,0.984414,0.901357,0.980232,0.915404,0.912918,0.868371,0.868292,0.993016,0.62425,0.203086,0.641296,0.525647,0.265704,0.553385,0.856376,0.879774,0.879064,0.210661,0.902068]
average_cost
0
kappa
-0.003787878787878788
kb_relative_information_score
1.4984793051042171
mean_absolute_error
0.019799712508792557
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
0.00625
prior_entropy
6.6438561897747395
recall
0.00625 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.375,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0625,0,0.0625]
relative_absolute_error
0.9999854802420465
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.09949734000554365
root_relative_squared_error
0.9999858922327449
total_cost
0
area_under_roc_curve
0.6060783377119656 [0.930818,0.281646,0.367089,0.861635,0.462025,0.710692,0.550633,0.937107,0.822785,0.698113,0.161392,0.256329,0.952532,0.136076,0.212025,0.691824,0.794304,0.341772,0.848101,0.91195,0.937107,0.320755,0.433962,0.867925,0.754717,0.81962,0.696203,0.68038,0.268987,0.383648,0.917722,0.376582,0.863924,0.227848,0.041139,0.886792,0.622642,0.660377,0.591195,0.075949,0,0.308176,0.594937,0.528481,0.724684,0.889241,0.169811,0.358491,0.825949,0.455696,0.993671,0.952532,0.389937,0.006329,0.679245,0.440252,0.867089,0.647799,0.358491,0.572785,0.792453,0.72327,0.227848,0.560127,0.433962,0.775316,0.446203,0.477987,0.666667,0.654088,0.712025,0.440252,0.629747,0.148734,0.572327,0.553797,0.85443,0.724684,0.436709,0.987342,0.936709,0.984177,1,1,0.933544,1,0.952532,1,0.990506,0.371069,0.259494,0.691824,0.531646,0.256329,0.522152,0.844937,0.981132,0.851266,0.129747,0.981132]
area_under_roc_curve
0.5826347424568109 [0.924528,0.259494,0.322785,0.805031,0.579114,0.861635,0.68038,0.90566,0.806962,0.874214,0.151899,0.085443,0.873418,0.623418,0.129747,0.641509,0.610759,0.21519,0.848101,0.748428,0.880503,0.226415,0.044025,0.962264,0.647799,0.936709,0.541139,0.974684,0.484177,0.201258,0.857595,0.174051,0.816456,0.468354,0.047468,0.522013,0.289308,0.603774,0.597484,0.113924,0,0.553459,0.664557,0.265823,0.268987,0.848101,0.176101,0.522013,0.870253,0.594937,0.946203,0.620253,0.503145,0.281646,0.314465,0.679245,0.778481,0.72327,0.371069,0.515823,0.578616,0.855346,0.107595,0.351266,0.358491,0.588608,0.468354,0.433962,0.849057,0.345912,0.367089,0.559748,0.724684,0.405063,0.81761,0.740506,0.629747,0.591772,0.468354,0.984177,0.955696,0.993671,0.91195,1,0.96519,0.990506,0.990506,0.990506,1,0.641509,0.053797,0.081761,0.462025,0.167722,0.262658,0.901899,0.981132,0.933544,0.262658,1]
area_under_roc_curve
0.5987468652973489 [0.962025,0.370253,0.335443,0.792453,0.601266,0.721519,0.698113,0.886076,0.898734,0.563291,0.170886,0.189873,0.924528,0.338608,0.291139,0.503165,0.908228,0.117089,0.842767,0.962025,0.937107,0.031447,0.838608,0.924528,0.496855,0.905063,0.639241,0.691824,0.43038,0.25,0.798742,0.458861,0.844937,0.287975,0.408805,0.591195,0.805031,0.779874,0.698113,0.167722,0.196203,0.616352,0.660377,0.358491,0.863924,0.748428,0.265823,0.522013,0.544304,0.5,0.987342,0.930818,0.534591,0.028481,0.45283,0.294304,0.849057,0.670886,0.301887,0.579114,0.220126,0.849057,0.322785,0.559748,0.177215,0.724684,0.582278,0.325949,0.987421,0.550633,0.550633,0.316456,0.471698,0.515823,0.930818,0.841772,0.556962,0.591772,0.313291,1,0.993711,0.987342,0.981013,0.971519,0.962264,1,1,0.993671,0.993671,0.867925,0.003165,0.125786,0.308176,0.373418,0.496835,0.886792,0.949367,0.89557,0.119497,0.971519]
area_under_roc_curve
0.5804061181434597 [0.901899,0.231013,0.481013,0.836478,0.617089,0.917722,0.327044,0.85443,0.598101,0.664557,0.186709,0.205696,0.836478,0.699367,0.243671,0.762658,0.721519,0.148734,0.81761,0.905063,0.91195,0.188679,0.756329,0.899371,0.679245,0.794304,0.572785,0.761006,0.439873,0.202532,0.823899,0.525316,0.803797,0.316456,0,0.742138,0.610063,0.534591,0.867925,0.06962,0.132911,0.408805,0.628931,0.402516,0.509494,0.352201,0.117089,0.597484,0.237342,0.566456,0.962025,0.943396,0.522013,0.10443,0.371069,0.240506,0.886792,0.772152,0.396226,0.879747,0.377358,0.132075,0.231013,0.584906,0.458861,0.651899,0.46519,0.227848,0.553459,0.60443,0.297468,0.462025,0.685535,0.287975,0.666667,0.487342,0.386076,0.825949,0.392405,1,0.974843,0.955696,0.927215,0.96519,1,0.955975,0.993711,0.993671,0.993671,0.792453,0.291139,0.867925,0.18239,0.313291,0.632911,0.893082,0.911392,0.917722,0.327044,0.943038]
area_under_roc_curve
0.5899012817450838 [0.924051,0.333333,0.424051,0.768987,0.779874,0.816456,0.138365,0.933544,0.918239,0.75,0.262658,0.234177,0.924528,0.490566,0.119497,0.670886,0.781646,0.325949,0.792453,0.75,0.841772,0.151899,0.39557,0.863924,0.455696,0.914557,0.405063,0.761006,0.389937,0.322785,0.811321,0.427673,0.848101,0.227848,0.025157,0.613924,0.566456,0.677215,0.743671,0.398734,0.060127,0.560127,0.622642,0.333333,0.861635,0.54717,0.43038,0.278481,0.617089,0.848101,0.962264,0.54717,0.338608,0.031447,0.553797,0.43038,0.861635,0.424051,0.31962,0.641509,0.161392,0.537975,0.201258,0.27673,0.560127,0.522152,0.503145,0.335443,0.727848,0.389241,0.227848,0.553797,0.842767,0.245283,0.693038,0.062893,0.632911,0.838608,0.481013,1,0.974843,0.990506,1,0.990506,0.993711,0.849057,0.955975,1,0.993671,0.810127,0.220126,0.914557,0.566038,0.150943,0.547468,0.918239,0.977848,0.899371,0.314465,0.727848]
area_under_roc_curve
0.5931118641031763 [0.955696,0.257862,0.525316,0.712025,0.710692,0.860759,0.830189,0.905063,0.924528,0.765823,0.098101,0.196203,0.880503,0.503145,0.176101,0.598101,0.724684,0.158228,0.830189,0.734177,0.803797,0.303797,0.503165,0.962025,0.613924,0.870253,0.71519,0.540881,0.125786,0.28481,0.874214,0.377358,0.870253,0.268987,0.188679,0.689873,0.601266,0.797468,0.848101,0.110759,0.018987,0.471519,0.748428,0.540881,0.842767,0.698113,0.018987,0.041139,0.955696,0.155063,0.981132,0.81761,0.46519,0.012579,0.436709,0.246835,0.924528,0.838608,0.344937,0.735849,0.259494,0.525316,0.408805,0.226415,0.110759,0.477848,0.440252,0.512658,0.693038,0.598101,0.509494,0.525316,0.698113,0.106918,0.734177,0.955975,0.341772,0.775316,0.509494,0.993711,0.974843,0.984177,0.996835,0.987342,0.880503,1,0.987421,0.993711,0.990506,0.825949,0.301887,0.693038,0.597484,0.314465,0.468354,0.855346,0.96519,0.861635,0.012579,0.927215]
area_under_roc_curve
0.6020184002069898 [0.911392,0.327044,0.503145,0.89557,0.672956,0.756329,0.537975,0.898734,0.943396,0.800633,0.18239,0.081761,0.952532,0.333333,0.081761,0.484177,0.742138,0.144654,0.816456,0.93038,0.813291,0.079114,0.768987,0.860759,0.813291,0.955975,0.45283,0.696203,0.119497,0.243671,0.860759,0.540881,0.823899,0.314465,0.047468,0.810127,0.556962,0.734177,0.683544,0.031447,0.031646,0.265823,0.693038,0.275316,0.836478,0.670886,0.25,0.221519,0.006289,0.037736,0.955975,0.927215,0.68038,0.056604,0.708861,0.186709,0.753165,0.797468,0.31962,0.710692,0.46519,0.414557,0.238994,0.408228,0.363924,0.63522,0.345912,0.313291,0.379747,0.411392,0.012579,0.575949,0.787975,0.27673,0.572785,0.742138,0.459119,0.811321,0.515723,0.993711,0.936709,1,0.993671,0.990506,0.892405,1,1,0.805031,1,0.629747,0.320755,0.727848,0.512658,0.169811,0.880503,0.936709,0.962025,0.91195,0.208861,0.955696]
area_under_roc_curve
0.6221088587692061 [0.974684,0.138365,0.641509,0.737342,0.647799,0.936709,0.572785,0.974684,0.207547,0.563291,0.188679,0.245283,0.96519,0.327044,0.301887,0.667722,0.679245,0.188679,0.93038,0.89557,0.939873,0.151899,0.512658,0.971519,0.825949,0.949686,0.295597,0.727848,0.559748,0.177215,0.917722,0.396226,0.918239,0.220126,0.234177,0.560127,0.617089,0.667722,0.768987,0.27673,0.009494,0.458861,0.64557,0.063291,0.679245,0.81962,0.379747,0.629747,0.899371,0.641509,0.433962,0.772152,0.268987,0.006289,0.455696,0.300633,0.825949,0.696203,0.367089,0.672956,0.351266,0.5,0.433962,0.386076,0.259494,0.685535,0.584906,0.237342,0.851266,0.363924,0.106918,0.525316,0.825949,0.220126,0.794304,0.616352,0.465409,0.754717,0.427673,0.987421,0.96519,0.987421,0.990506,0.990506,0.841772,0.996835,0.996835,0.993711,1,0.712025,0.301887,0.775316,0.863924,0.207547,0.937107,0.927215,0.949367,0.861635,0.205696,0.93038]
area_under_roc_curve
0.6198635657989013 [0.943396,0.231013,0.534591,0.794304,0.718354,0.993711,0.677215,0.893082,0.727848,0.867925,0.081761,0.062893,0.968354,0.446203,0.256329,0.685535,0.754717,0.176101,0.933544,0.886792,0.89557,0.136076,0.559748,0.898734,0.712025,0.981132,0.333333,0.667722,0.449367,0.056604,0.851266,0.300633,0.899371,0.226415,0.085443,0.699367,0.734177,0.775316,0.68038,0.163522,0.138365,0.43038,0.53481,0.310127,0.541139,0.408228,0.496855,0.300633,0.805031,0.823899,0.848101,0.727848,0.642405,0.161392,0.677215,0.540881,0.775316,0.660377,0.360759,0.882911,0.379747,0.737342,0.411392,0.272152,0.069182,0.968553,0.610759,0.08805,0.724684,0.584906,0.540881,0.440252,0.870253,0.281646,0.620253,0.977848,0.540881,0.949686,0.408805,1,0.971519,0.993711,1,1,0.939873,0.996835,1,0.829114,0.974843,0.658228,0.196203,0.787975,0.743671,0.158228,0.899371,0.797468,0.90566,0.781646,0.177215,0.918239]
area_under_roc_curve
0.5806317669771516 [0.836478,0.25,0.339623,0.753165,0.800633,0.710692,0.316456,0.993711,0.582278,0.72956,0.289308,0.100629,0.844937,0.458861,0.306962,0.735849,0.716981,0.301887,0.860759,0.918239,0.943038,0.101266,0.320755,0.822785,0.75,0.943396,0.345912,0.651899,0.325949,0.132075,0.905063,0.449367,0.792453,0.333333,0.123418,0.566456,0.585443,0.677215,0.677215,0.044025,0.025157,0.759494,0.575949,0.120253,0.518987,0.56962,0.490566,0.487342,0.597484,0.773585,0.838608,0.689873,0.556962,0.082278,0.348101,0.358491,0.806962,0.72956,0.06962,0.920886,0.439873,0.724684,0.262658,0.484177,0.27044,0.691824,0.471519,0.301887,0.841772,0.396226,0.811321,0.169811,0.705696,0.186709,0.509494,0.759494,0.559748,0.314465,0.559748,0.984177,0.984177,0.974843,1,0.987421,0.981013,0.996835,0.993671,0.974684,0.987421,0.424051,0.082278,0.39557,0.341772,0.417722,0.352201,0.933544,0.874214,0.873418,0.268987,0.81761]
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
-0.005493853997865698
kappa
-0.005136309758988542
kappa
0.007585335018963337
kappa
-0.01149788612746454
kappa
0.00797725298159703
kappa
0.0011063695274221598
kappa
-0.008946517952153865
kappa
-0.009026053368018604
kappa
-0.008390120928034032
kappa
-0.008429843220672811
kb_relative_information_score
0.14777579676870084
kb_relative_information_score
0.1418946430997584
kb_relative_information_score
0.13994295586914668
kb_relative_information_score
0.13725634378655466
kb_relative_information_score
0.14388604571429517
kb_relative_information_score
0.1470986632451423
kb_relative_information_score
0.1610027864591011
kb_relative_information_score
0.16772168233866114
kb_relative_information_score
0.15628513742226766
kb_relative_information_score
0.15561525040058757
mean_absolute_error
0.01979972292337725
mean_absolute_error
0.019799759322745136
mean_absolute_error
0.01979980145602158
mean_absolute_error
0.019799819834183198
mean_absolute_error
0.0197997767173139
mean_absolute_error
0.0197997824639288
mean_absolute_error
0.01979959960975215
mean_absolute_error
0.019799568292435274
mean_absolute_error
0.019799622567321654
mean_absolute_error
0.019799671900846673
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
0.00625
predictive_accuracy
0.00625
predictive_accuracy
0.01875
predictive_accuracy
0
predictive_accuracy
0.01875
predictive_accuracy
0.0125
predictive_accuracy
0
predictive_accuracy
0
predictive_accuracy
0
predictive_accuracy
0
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
0.00625 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
recall
0.00625 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
recall
0.01875 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5]
recall
0 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
recall
0.01875 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]
recall
0.0125 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
recall
0 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
recall
0 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
recall
0 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
recall
0 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
relative_absolute_error
0.9999860062311724
relative_absolute_error
0.9999878445830861
relative_absolute_error
0.9999899725263407
relative_absolute_error
0.9999909007163216
relative_absolute_error
0.99998872309666
relative_absolute_error
0.9999890133297356
relative_absolute_error
0.9999797782703089
relative_absolute_error
0.9999781965876384
relative_absolute_error
0.9999809377435163
relative_absolute_error
0.999983429335689
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
0.09949739369026118
root_mean_squared_error
0.0994975759089021
root_mean_squared_error
0.0994977859753486
root_mean_squared_error
0.09949787812550273
root_mean_squared_error
0.09949766141114752
root_mean_squared_error
0.09949769063217119
root_mean_squared_error
0.09949677248827411
root_mean_squared_error
0.09949661653161383
root_mean_squared_error
0.0994968887794427
root_mean_squared_error
0.09949713650340654
root_relative_squared_error
0.9999864317844576
root_relative_squared_error
0.9999882631507052
root_relative_squared_error
0.9999903743979296
root_relative_squared_error
0.9999913005418254
root_relative_squared_error
0.9999891224806045
root_relative_squared_error
0.9999894161629423
root_relative_squared_error
0.9999801884695784
root_relative_squared_error
0.9999786210461672
root_relative_squared_error
0.9999813572397984
root_relative_squared_error
0.9999838469593123
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
24563.442663999012
usercpu_time_millis
24606.23922099967
usercpu_time_millis
24575.141154999983
usercpu_time_millis
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usercpu_time_millis
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usercpu_time_millis
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usercpu_time_millis
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usercpu_time_millis
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usercpu_time_millis
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usercpu_time_millis
24737.075993999497
usercpu_time_millis_testing
17.211479999787116
usercpu_time_millis_testing
12.89981700028875
usercpu_time_millis_testing
13.307060999977693
usercpu_time_millis_testing
14.596125000025495
usercpu_time_millis_testing
13.331073999324872
usercpu_time_millis_testing
16.0246619998361
usercpu_time_millis_testing
13.754931999756081
usercpu_time_millis_testing
12.86258199979784
usercpu_time_millis_testing
15.598033999594918
usercpu_time_millis_testing
13.096550999762258
usercpu_time_millis_training
24546.231183999225
usercpu_time_millis_training
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usercpu_time_millis_training
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usercpu_time_millis_training
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usercpu_time_millis_training
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usercpu_time_millis_training
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usercpu_time_millis_training
24620.64747000022
usercpu_time_millis_training
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usercpu_time_millis_training
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usercpu_time_millis_training
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