10576234
28997
Marc Boel
16
Supervised Classification
19037
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier)(1)
8292516
Python_3.8.10. Sklearn_0.24.2. NumPy_1.17.4. SciPy_1.3.3.
add_indicator
false
18819
copy
true
18819
fill_value
null
18819
missing_values
NaN
18819
strategy
"most_frequent"
18819
verbose
0
18819
n_jobs
null
18996
remainder
"drop"
18996
sparse_threshold
0.3
18996
transformer_weights
null
18996
transformers
[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}]
18996
verbose
false
18996
categories
"auto"
18997
drop
null
18997
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
18997
handle_unknown
"ignore"
18997
sparse
true
18997
memory
null
19037
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}]
19037
verbose
false
19037
ccp_alpha
0.0
19038
criterion
"friedman_mse"
19038
init
null
19038
learning_rate
0.6882440565138986
19038
loss
"deviance"
19038
max_depth
3
19038
max_features
null
19038
max_leaf_nodes
111
19038
min_impurity_decrease
0.0
19038
min_impurity_split
null
19038
min_samples_leaf
53
19038
min_samples_split
2
19038
min_weight_fraction_leaf
0.0
19038
n_estimators
100
19038
n_iter_no_change
1
19038
random_state
28827
19038
subsample
1.0
19038
tol
0.0001
19038
validation_fraction
0.3066140473238077
19038
verbose
0
19038
warm_start
false
19038
openml-python
Sklearn_0.24.2.
16
mfeat-karhunen
https://www.openml.org/data/download/16/dataset_16_mfeat-karhunen.arff
-1
22078735
description
https://api.openml.org/data/download/22078735/description.xml
-1
22078737
predictions
https://api.openml.org/data/download/22078737/predictions.arff
area_under_roc_curve
0.9942619444444443 [0.999281,0.993792,0.999619,0.992519,0.996603,0.987664,0.9917,0.997075,0.991853,0.992514]
average_cost
0
f_measure
0.9199320660495018 [0.967742,0.879012,0.962406,0.886076,0.938272,0.880597,0.928934,0.945545,0.891139,0.919598]
kappa
0.9111111111111112
kb_relative_information_score
0.9199521524481291
mean_absolute_error
0.01831884339499314
mean_prior_absolute_error
0.18000000000000554
weighted_recall
0.92 [0.975,0.89,0.96,0.875,0.95,0.885,0.915,0.955,0.88,0.915]
number_of_instances
2000 [200,200,200,200,200,200,200,200,200,200]
precision
0.920059073170834 [0.960591,0.868293,0.964824,0.897436,0.926829,0.876238,0.943299,0.936275,0.902564,0.924242]
predictive_accuracy
0.92
prior_entropy
3.3219280948872383
relative_absolute_error
0.1017713521944032
root_mean_prior_squared_error
0.3000000000000046
root_mean_squared_error
0.11006382691931582
root_relative_squared_error
0.3668794230643805
total_cost
0
unweighted_recall
0.9199999999999999 [0.975,0.89,0.96,0.875,0.95,0.885,0.915,0.955,0.88,0.915]
area_under_roc_curve
0.9954444444444445 [0.998611,0.989167,1,0.997222,0.999722,0.993611,0.999722,1,0.979167,0.997222]
area_under_roc_curve
0.9945277777777777 [1,0.995556,1,0.989444,1,0.991667,0.994167,0.988611,0.998889,0.986944]
area_under_roc_curve
0.9961111111111113 [0.999722,0.996667,0.999444,0.994444,0.997778,0.986111,0.999167,0.995278,0.996111,0.996389]
area_under_roc_curve
0.9968333333333332 [1,0.9975,1,0.998611,0.991111,0.990833,0.995833,1,0.996667,0.997778]
area_under_roc_curve
0.9921111111111113 [0.999444,0.990278,0.999167,0.981389,0.991667,0.967778,0.998611,0.999167,0.993889,0.999722]
area_under_roc_curve
0.9932777777777778 [1,0.995833,0.998056,0.998333,0.991944,0.989444,0.996111,0.996667,0.993333,0.973056]
area_under_roc_curve
0.9907777777777779 [0.994444,0.985,0.998611,0.9925,1,0.991111,1,0.996111,0.983333,0.966667]
area_under_roc_curve
0.9972777777777777 [1,0.991111,0.999722,0.994722,0.999722,0.993056,0.998889,0.999167,0.996389,1]
area_under_roc_curve
0.997 [1,0.998333,1,0.985556,0.999444,0.988333,0.998333,1,1,1]
area_under_roc_curve
0.9908888888888887 [1,0.993889,1,0.994722,1,1,0.941667,0.999444,0.981944,0.997222]
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
0.935316222074887 [0.974359,0.878049,1,0.9,0.95,0.904762,0.947368,1,0.871795,0.926829]
f_measure
0.9349301725232405 [1,0.95,1,0.829268,0.974359,0.842105,0.974359,0.926829,0.952381,0.9]
f_measure
0.8939247286615708 [0.952381,0.810811,0.95,0.871795,0.857143,0.85,0.952381,0.894737,0.9,0.9]
f_measure
0.9352812848255724 [1,0.95,0.97561,0.894737,0.926829,0.923077,0.878049,1,0.857143,0.947368]
f_measure
0.8945815370705659 [0.95,0.780488,0.894737,0.820513,0.9,0.904762,0.947368,0.930233,0.842105,0.97561]
f_measure
0.9141295781668053 [0.952381,0.904762,0.926829,0.9,0.95,0.923077,0.871795,0.97561,0.842105,0.894737]
f_measure
0.9154346657909663 [0.947368,0.837209,0.947368,0.95,1,0.878049,1,0.878049,0.926829,0.789474]
f_measure
0.9133906063353475 [0.97561,0.820513,0.95,0.871795,0.930233,0.842105,0.95,0.952381,0.888889,0.952381]
f_measure
0.9394043815995035 [0.952381,0.9,1,0.9,0.926829,0.864865,0.9,0.97561,0.974359,1]
f_measure
0.9203134581448473 [0.974359,0.952381,0.97561,0.923077,0.974359,0.869565,0.864865,0.918919,0.85,0.9]
kappa
0.9277777777777778
kappa
0.9277777777777778
kappa
0.8833333333333333
kappa
0.9277777777777778
kappa
0.8833333333333333
kappa
0.9055555555555556
kappa
0.9055555555555556
kappa
0.9055555555555556
kappa
0.9333333333333332
kappa
0.9111111111111112
kb_relative_information_score
0.9306967354465148
kb_relative_information_score
0.9356297613835092
kb_relative_information_score
0.9054718431549952
kb_relative_information_score
0.9389417945578458
kb_relative_information_score
0.9014816666670956
kb_relative_information_score
0.9080443286334521
kb_relative_information_score
0.8997142542383455
kb_relative_information_score
0.9139929937566247
kb_relative_information_score
0.9435234887453021
kb_relative_information_score
0.9220246578972968
mean_absolute_error
0.015565065358952685
mean_absolute_error
0.014397214035920624
mean_absolute_error
0.022297149842543513
mean_absolute_error
0.013495466270638407
mean_absolute_error
0.023504904029745272
mean_absolute_error
0.020829673080990076
mean_absolute_error
0.023717873878110654
mean_absolute_error
0.019558645545872336
mean_absolute_error
0.012600607806475204
mean_absolute_error
0.017221834100682957
mean_prior_absolute_error
0.1799999999999998
mean_prior_absolute_error
0.1799999999999998
mean_prior_absolute_error
0.1799999999999998
mean_prior_absolute_error
0.1799999999999998
mean_prior_absolute_error
0.1799999999999998
mean_prior_absolute_error
0.1799999999999998
mean_prior_absolute_error
0.1799999999999998
mean_prior_absolute_error
0.1799999999999998
mean_prior_absolute_error
0.1799999999999998
mean_prior_absolute_error
0.1799999999999998
number_of_instances
200 [20,20,20,20,20,20,20,20,20,20]
number_of_instances
200 [20,20,20,20,20,20,20,20,20,20]
number_of_instances
200 [20,20,20,20,20,20,20,20,20,20]
number_of_instances
200 [20,20,20,20,20,20,20,20,20,20]
number_of_instances
200 [20,20,20,20,20,20,20,20,20,20]
number_of_instances
200 [20,20,20,20,20,20,20,20,20,20]
number_of_instances
200 [20,20,20,20,20,20,20,20,20,20]
number_of_instances
200 [20,20,20,20,20,20,20,20,20,20]
number_of_instances
200 [20,20,20,20,20,20,20,20,20,20]
number_of_instances
200 [20,20,20,20,20,20,20,20,20,20]
precision
0.9370277967646389 [1,0.857143,1,0.9,0.95,0.863636,1,1,0.894737,0.904762]
precision
0.9362265512265513 [1,0.95,1,0.809524,1,0.888889,1,0.904762,0.909091,0.9]
precision
0.8957897864089814 [0.909091,0.882353,0.95,0.894737,0.818182,0.85,0.909091,0.944444,0.9,0.9]
precision
0.9374280397964608 [1,0.95,0.952381,0.944444,0.904762,0.947368,0.857143,1,0.818182,1]
precision
0.897292589180461 [0.95,0.761905,0.944444,0.842105,0.9,0.863636,1,0.869565,0.888889,0.952381]
precision
0.9155308726361356 [0.909091,0.863636,0.904762,0.9,0.95,0.947368,0.894737,0.952381,0.888889,0.944444]
precision
0.9184989648033125 [1,0.782609,1,0.95,1,0.857143,1,0.857143,0.904762,0.833333]
precision
0.9165858982106121 [0.952381,0.842105,0.95,0.894737,0.869565,0.888889,0.95,0.909091,1,0.909091]
precision
0.9407410236822003 [0.909091,0.9,1,0.9,0.904762,0.941176,0.9,0.952381,1,1]
precision
0.9269247522343497 [1,0.909091,0.952381,0.947368,1,0.769231,0.941176,1,0.85,0.9]
predictive_accuracy
0.935
predictive_accuracy
0.935
predictive_accuracy
0.895
predictive_accuracy
0.935
predictive_accuracy
0.895
predictive_accuracy
0.915
predictive_accuracy
0.915
predictive_accuracy
0.915
predictive_accuracy
0.94
predictive_accuracy
0.92
prior_entropy
3.321928094887355
prior_entropy
3.321928094887355
prior_entropy
3.321928094887355
prior_entropy
3.321928094887355
prior_entropy
3.321928094887355
prior_entropy
3.321928094887355
prior_entropy
3.321928094887355
prior_entropy
3.321928094887355
prior_entropy
3.321928094887355
prior_entropy
3.321928094887355
relative_absolute_error
0.08647258532751502
relative_absolute_error
0.07998452242178133
relative_absolute_error
0.12387305468079743
relative_absolute_error
0.0749748126146579
relative_absolute_error
0.13058280016525167
relative_absolute_error
0.11572040600550056
relative_absolute_error
0.13176596598950377
relative_absolute_error
0.1086591419215131
relative_absolute_error
0.07000337670264009
relative_absolute_error
0.09567685611490542
root_mean_prior_squared_error
0.2999999999999998
root_mean_prior_squared_error
0.2999999999999998
root_mean_prior_squared_error
0.2999999999999998
root_mean_prior_squared_error
0.2999999999999998
root_mean_prior_squared_error
0.2999999999999998
root_mean_prior_squared_error
0.2999999999999998
root_mean_prior_squared_error
0.2999999999999998
root_mean_prior_squared_error
0.2999999999999998
root_mean_prior_squared_error
0.2999999999999998
root_mean_prior_squared_error
0.2999999999999998
root_mean_squared_error
0.1005110197585496
root_mean_squared_error
0.10206433113809343
root_mean_squared_error
0.11784930244375837
root_mean_squared_error
0.10035171595459663
root_mean_squared_error
0.1226972241755272
root_mean_squared_error
0.11533986001374698
root_mean_squared_error
0.11976777410464158
root_mean_squared_error
0.11334724102618077
root_mean_squared_error
0.09390921219294072
root_mean_squared_error
0.11087467326162435
root_relative_squared_error
0.33503673252849886
root_relative_squared_error
0.3402144371269783
root_relative_squared_error
0.3928310081458614
root_relative_squared_error
0.3345057198486557
root_relative_squared_error
0.4089907472517576
root_relative_squared_error
0.3844662000458235
root_relative_squared_error
0.3992259136821389
root_relative_squared_error
0.3778241367539361
root_relative_squared_error
0.31303070730980265
root_relative_squared_error
0.36958224420541474
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
unweighted_recall
0.9349999999999999 [0.95,0.9,1,0.9,0.95,0.95,0.9,1,0.85,0.95]
unweighted_recall
0.9349999999999999 [1,0.95,1,0.85,0.95,0.8,0.95,0.95,1,0.9]
unweighted_recall
0.8949999999999999 [1,0.75,0.95,0.85,0.9,0.85,1,0.85,0.9,0.9]
unweighted_recall
0.9350000000000002 [1,0.95,1,0.85,0.95,0.9,0.9,1,0.9,0.9]
unweighted_recall
0.8950000000000001 [0.95,0.8,0.85,0.8,0.9,0.95,0.9,1,0.8,1]
unweighted_recall
0.915 [1,0.95,0.95,0.9,0.95,0.9,0.85,1,0.8,0.85]
unweighted_recall
0.915 [0.9,0.9,0.9,0.95,1,0.9,1,0.9,0.95,0.75]
unweighted_recall
0.915 [1,0.8,0.95,0.85,1,0.8,0.95,1,0.8,1]
unweighted_recall
0.9400000000000001 [1,0.9,1,0.9,0.95,0.8,0.9,1,0.95,1]
unweighted_recall
0.9199999999999999 [0.95,1,1,0.9,0.95,1,0.8,0.85,0.85,0.9]
usercpu_time_millis
3773.367046000203
usercpu_time_millis
3758.7447460000476
usercpu_time_millis
3199.7971400005554
usercpu_time_millis
4002.027246999205
usercpu_time_millis
3302.2068410009524
usercpu_time_millis
2541.6196299993317
usercpu_time_millis
2379.1372300001967
usercpu_time_millis
2854.7340360000817
usercpu_time_millis
3626.7557460005264
usercpu_time_millis
3145.317636999607
usercpu_time_millis_testing
2.659200000380224
usercpu_time_millis_testing
2.4819999998726416
usercpu_time_millis_testing
2.6324999998905696
usercpu_time_millis_testing
2.4116999993566424
usercpu_time_millis_testing
3.045100000235834
usercpu_time_millis_testing
2.4808999996821512
usercpu_time_millis_testing
3.041299999495095
usercpu_time_millis_testing
3.2363999998779036
usercpu_time_millis_testing
2.4934000002758694
usercpu_time_millis_testing
2.4868000000424217
usercpu_time_millis_training
3770.707845999823
usercpu_time_millis_training
3756.262746000175
usercpu_time_millis_training
3197.164640000665
usercpu_time_millis_training
3999.6155469998484
usercpu_time_millis_training
3299.1617410007166
usercpu_time_millis_training
2539.1387299996495
usercpu_time_millis_training
2376.0959300007016
usercpu_time_millis_training
2851.497636000204
usercpu_time_millis_training
3624.2623460002505
usercpu_time_millis_training
3142.830836999565
wall_clock_time_millis
3782.2272777557373
wall_clock_time_millis
3761.240243911743
wall_clock_time_millis
3202.012538909912
wall_clock_time_millis
4003.981351852417
wall_clock_time_millis
3302.950620651245
wall_clock_time_millis
2562.913179397583
wall_clock_time_millis
2379.375219345093
wall_clock_time_millis
2856.6198348999023
wall_clock_time_millis
3627.286195755005
wall_clock_time_millis
3157.5703620910645
wall_clock_time_millis_testing
2.663135528564453
wall_clock_time_millis_testing
2.485513687133789
wall_clock_time_millis_testing
2.6357173919677734
wall_clock_time_millis_testing
2.415180206298828
wall_clock_time_millis_testing
3.072023391723633
wall_clock_time_millis_testing
2.483844757080078
wall_clock_time_millis_testing
3.0455589294433594
wall_clock_time_millis_testing
3.2401084899902344
wall_clock_time_millis_testing
2.496480941772461
wall_clock_time_millis_testing
2.4900436401367188
wall_clock_time_millis_training
3779.564142227173
wall_clock_time_millis_training
3758.7547302246094
wall_clock_time_millis_training
3199.3768215179443
wall_clock_time_millis_training
4001.566171646118
wall_clock_time_millis_training
3299.8785972595215
wall_clock_time_millis_training
2560.429334640503
wall_clock_time_millis_training
2376.3296604156494
wall_clock_time_millis_training
2853.379726409912
wall_clock_time_millis_training
3624.7897148132324
wall_clock_time_millis_training
3155.0803184509277
weighted_recall
0.935 [0.95,0.9,1,0.9,0.95,0.95,0.9,1,0.85,0.95]
weighted_recall
0.935 [1,0.95,1,0.85,0.95,0.8,0.95,0.95,1,0.9]
weighted_recall
0.895 [1,0.75,0.95,0.85,0.9,0.85,1,0.85,0.9,0.9]
weighted_recall
0.935 [1,0.95,1,0.85,0.95,0.9,0.9,1,0.9,0.9]
weighted_recall
0.895 [0.95,0.8,0.85,0.8,0.9,0.95,0.9,1,0.8,1]
weighted_recall
0.915 [1,0.95,0.95,0.9,0.95,0.9,0.85,1,0.8,0.85]
weighted_recall
0.915 [0.9,0.9,0.9,0.95,1,0.9,1,0.9,0.95,0.75]
weighted_recall
0.915 [1,0.8,0.95,0.85,1,0.8,0.95,1,0.8,1]
weighted_recall
0.94 [1,0.9,1,0.9,0.95,0.8,0.9,1,0.95,1]
weighted_recall
0.92 [0.95,1,1,0.9,0.95,1,0.8,0.85,0.85,0.9]