10416338
8323
Heinrich Peters
23
Supervised Classification
16374
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC)(2)
8254041
add_indicator
false
12737
copy
true
12737
fill_value
null
12737
missing_values
NaN
12737
strategy
"most_frequent"
12737
verbose
0
12737
copy
true
13294
with_mean
true
13294
with_std
true
13294
C
81356.92108932823
13389
cache_size
200
13389
class_weight
null
13389
coef0
-0.33706310267468553
13389
decision_function_shape
"ovr"
13389
degree
2
13389
gamma
2.238549391298506
13389
kernel
"poly"
13389
max_iter
-1
13389
probability
false
13389
random_state
1
13389
shrinking
true
13389
tol
0.001
13389
verbose
false
13389
categorical_features
null
16348
categories
null
16348
drop
null
16348
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
16348
handle_unknown
"ignore"
16348
n_values
null
16348
sparse
true
16348
memory
null
16374
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}]
16374
verbose
false
16374
n_jobs
null
16375
remainder
"drop"
16375
sparse_threshold
0.3
16375
transformer_weights
null
16375
transformers
[{"oml-python:serialized_object": "component_reference", "value": {"key": "num", "step_name": "num", "argument_1": [true, false, false, true, false, false, false, false, false]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [false, true, true, false, true, true, true, true, true]}}]
16375
verbose
false
16375
memory
null
16376
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
16376
verbose
false
16376
memory
null
16377
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
16377
verbose
false
16377
openml-python
Sklearn_0.21.2.
23
cmc
https://www.openml.org/data/download/23/dataset_23_cmc.arff
-1
21754542
description
https://api.openml.org/data/download/21754542/description.xml
-1
21754543
predictions
https://api.openml.org/data/download/21754543/predictions.arff
area_under_roc_curve
0.6136451257366697 [0.632651,0.596661,0.601319]
average_cost
0
f_measure
0.5004261987245193 [0.588326,0.372457,0.475622]
kappa
0.22544968679960925
kb_relative_information_score
0.28526636961890584
mean_absolute_error
0.3312966734555364
mean_prior_absolute_error
0.4308274628344832
number_of_instances
1473 [629,333,511]
precision
0.49876753393451945 [0.569094,0.388889,0.483806]
predictive_accuracy
0.5030549898167006
prior_entropy
1.5390347976697278
recall
0.5030549898167006 [0.608903,0.357357,0.46771]
relative_absolute_error
0.7689776117703413
root_mean_prior_squared_error
0.46411194018393376
root_mean_squared_error
0.5755837675399962
root_relative_squared_error
1.24018306297374
total_cost
0
area_under_roc_curve
0.6349627099890417 [0.658357,0.605005,0.626036]
area_under_roc_curve
0.6318257742597854 [0.642764,0.621001,0.625531]
area_under_roc_curve
0.599380272999053 [0.639216,0.56063,0.576006]
area_under_roc_curve
0.5820018796992482 [0.611111,0.55941,0.560662]
area_under_roc_curve
0.6216322055137845 [0.638889,0.590112,0.620711]
area_under_roc_curve
0.6139176065162908 [0.64881,0.624402,0.564032]
area_under_roc_curve
0.6275454260651628 [0.652778,0.626396,0.59712]
area_under_roc_curve
0.568765664160401 [0.583333,0.550638,0.5625]
area_under_roc_curve
0.6082393483709273 [0.625,0.605263,0.589461]
area_under_roc_curve
0.647265221878225 [0.624288,0.624402,0.68917]
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
0.5223708765446999 [0.601626,0.394366,0.509804]
f_measure
0.5247741044616046 [0.59375,0.412698,0.514286]
f_measure
0.4798340149121069 [0.608696,0.290909,0.446602]
f_measure
0.458118556701031 [0.571429,0.3125,0.412371]
f_measure
0.5137146991189313 [0.614286,0.350877,0.494845]
f_measure
0.49873625706006136 [0.590164,0.41791,0.438095]
f_measure
0.5132818632765684 [0.616541,0.422535,0.444444]
f_measure
0.4408472479901051 [0.557143,0.30303,0.386364]
f_measure
0.49817415028316403 [0.586466,0.37931,0.466019]
f_measure
0.5350850639690381 [0.535714,0.41791,0.608696]
kappa
0.26337188923939714
kappa
0.26362925581064744
kappa
0.19640917975819883
kappa
0.16030368763557484
kappa
0.24449339207048465
kappa
0.22388698630136988
kappa
0.25214961306964745
kappa
0.13604701784936873
kappa
0.21797114123305641
kappa
0.2916164694210191
kb_relative_information_score
0.3134884798845469
kb_relative_information_score
0.32076108392045777
kb_relative_information_score
0.2637298662528939
kb_relative_information_score
0.2257352077318021
kb_relative_information_score
0.3085768072087569
kb_relative_information_score
0.2806815342855123
kb_relative_information_score
0.3083060027260108
kb_relative_information_score
0.20629077720194128
kb_relative_information_score
0.28375220036378085
kb_relative_information_score
0.3408680692926231
mean_absolute_error
0.3198198198198196
mean_absolute_error
0.3153153153153151
mean_absolute_error
0.33783783783783755
mean_absolute_error
0.35827664399092934
mean_absolute_error
0.3174603174603173
mean_absolute_error
0.3356009070294782
mean_absolute_error
0.3219954648526075
mean_absolute_error
0.36734693877550983
mean_absolute_error
0.33106575963718793
mean_absolute_error
0.3083900226757368
mean_prior_absolute_error
0.4311933885104624
mean_prior_absolute_error
0.4311933885104624
mean_prior_absolute_error
0.4311933885104624
mean_prior_absolute_error
0.43061777556551145
mean_prior_absolute_error
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mean_prior_absolute_error
0.43061777556551145
mean_prior_absolute_error
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mean_prior_absolute_error
0.43061777556551145
mean_prior_absolute_error
0.43061777556551145
mean_prior_absolute_error
0.4309803415494479
number_of_instances
148 [63,34,51]
number_of_instances
148 [63,34,51]
number_of_instances
148 [63,34,51]
number_of_instances
147 [63,33,51]
number_of_instances
147 [63,33,51]
number_of_instances
147 [63,33,51]
number_of_instances
147 [63,33,51]
number_of_instances
147 [63,33,51]
number_of_instances
147 [63,33,51]
number_of_instances
147 [62,33,52]
precision
0.525100438276114 [0.616667,0.378378,0.509804]
precision
0.5241361387913113 [0.584615,0.448276,0.5]
precision
0.47831130581130576 [0.56,0.380952,0.442308]
precision
0.45591207029796493 [0.542857,0.322581,0.434783]
precision
0.5138810464897421 [0.558442,0.416667,0.521739]
precision
0.5017084046424596 [0.610169,0.411765,0.425926]
precision
0.5175521220634003 [0.585714,0.394737,0.512821]
precision
0.44450015878587307 [0.506494,0.30303,0.459459]
precision
0.49767660910518063 [0.557143,0.44,0.461538]
precision
0.5420212529456226 [0.6,0.411765,0.555556]
predictive_accuracy
0.5202702702702703
predictive_accuracy
0.527027027027027
predictive_accuracy
0.4932432432432432
predictive_accuracy
0.4625850340136054
predictive_accuracy
0.5238095238095238
predictive_accuracy
0.4965986394557823
predictive_accuracy
0.5170068027210885
predictive_accuracy
0.4489795918367347
predictive_accuracy
0.5034013605442177
predictive_accuracy
0.5374149659863946
prior_entropy
1.5416928769893397
prior_entropy
1.5416928769893397
prior_entropy
1.5416928769893397
prior_entropy
1.5375970957879994
prior_entropy
1.5375970957879994
prior_entropy
1.5375970957879994
prior_entropy
1.5375970957879994
prior_entropy
1.5375970957879994
prior_entropy
1.5375970957879994
prior_entropy
1.5396325244845115
recall
0.5202702702702703 [0.587302,0.411765,0.509804]
recall
0.527027027027027 [0.603175,0.382353,0.529412]
recall
0.49324324324324326 [0.666667,0.235294,0.45098]
recall
0.46258503401360546 [0.603175,0.30303,0.392157]
recall
0.5238095238095238 [0.68254,0.30303,0.470588]
recall
0.4965986394557823 [0.571429,0.424242,0.45098]
recall
0.5170068027210885 [0.650794,0.454545,0.392157]
recall
0.4489795918367347 [0.619048,0.30303,0.333333]
recall
0.5034013605442177 [0.619048,0.333333,0.470588]
recall
0.5374149659863946 [0.483871,0.424242,0.673077]
relative_absolute_error
0.741708542713566
relative_absolute_error
0.7312619435204172
relative_absolute_error
0.7834949394861611
relative_absolute_error
0.8320061649113771
relative_absolute_error
0.7372206524531194
relative_absolute_error
0.7793475468790118
relative_absolute_error
0.7477523760595923
relative_absolute_error
0.8530696121243234
relative_absolute_error
0.7688158232725385
relative_absolute_error
0.7155547317240085
root_mean_prior_squared_error
0.46450599425333045
root_mean_prior_squared_error
0.46450599425333045
root_mean_prior_squared_error
0.46450599425333045
root_mean_prior_squared_error
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root_mean_prior_squared_error
0.4638859835695881
root_mean_prior_squared_error
0.4638859835695881
root_mean_prior_squared_error
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0.4638859835695881
root_mean_prior_squared_error
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root_mean_prior_squared_error
0.4642766112311287
root_mean_squared_error
0.5655261442407589
root_mean_squared_error
0.5615294429638709
root_mean_squared_error
0.5812381937190961
root_mean_squared_error
0.5985621471417395
root_mean_squared_error
0.5634361698190109
root_mean_squared_error
0.5793107171712588
root_mean_squared_error
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root_mean_squared_error
0.6060915267313262
root_mean_squared_error
0.5753831415997412
root_mean_squared_error
0.5553287518900285
root_relative_squared_error
1.2174786789346241
root_relative_squared_error
1.2088744815155734
root_relative_squared_error
1.2513039678926139
root_relative_squared_error
1.290321691842945
root_relative_squared_error
1.2146005479264264
root_relative_squared_error
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root_relative_squared_error
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root_relative_squared_error
1.3065527914154051
root_relative_squared_error
1.2403546603675886
root_relative_squared_error
1.1961161481244027
total_cost
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usercpu_time_millis
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