10576155
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)
8292437
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
"mean"
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
0.5823557371711975
19038
loss
"deviance"
19038
max_depth
3
19038
max_features
null
19038
max_leaf_nodes
1817
19038
min_impurity_decrease
0.0
19038
min_impurity_split
null
19038
min_samples_leaf
159
19038
min_samples_split
2
19038
min_weight_fraction_leaf
0.0
19038
n_estimators
100
19038
n_iter_no_change
7
19038
random_state
32559
19038
subsample
1.0
19038
tol
0.0001
19038
validation_fraction
0.12321250256377692
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
22078577
description
https://api.openml.org/data/download/22078577/description.xml
-1
22078578
predictions
https://api.openml.org/data/download/22078578/predictions.arff
area_under_roc_curve
0.9973408333333333 [0.999433,0.997831,0.999903,0.996267,0.998036,0.995464,0.995044,0.998794,0.995586,0.99705]
average_cost
0
f_measure
0.9514902759902966 [0.97,0.935961,0.982544,0.945,0.95,0.927681,0.964646,0.967742,0.933673,0.937656]
kappa
0.9461111111111111
kb_relative_information_score
0.9472847983172695
mean_absolute_error
0.01272488088271317
mean_prior_absolute_error
0.18000000000000554
weighted_recall
0.9515 [0.97,0.95,0.985,0.945,0.95,0.93,0.955,0.975,0.915,0.94]
number_of_instances
2000 [200,200,200,200,200,200,200,200,200,200]
precision
0.9516332045911171 [0.97,0.92233,0.9801,0.945,0.95,0.925373,0.97449,0.960591,0.953125,0.935323]
predictive_accuracy
0.9515
prior_entropy
3.3219280948872383
relative_absolute_error
0.07069378268173765
root_mean_prior_squared_error
0.3000000000000046
root_mean_squared_error
0.088871302846153
root_relative_squared_error
0.2962376761538388
total_cost
0
unweighted_recall
0.9514999999999999 [0.97,0.95,0.985,0.945,0.95,0.93,0.955,0.975,0.915,0.94]
area_under_roc_curve
0.9973333333333333 [0.998333,0.995833,1,0.999444,0.999722,0.994167,0.999722,1,0.986389,0.999722]
area_under_roc_curve
0.996861111111111 [1,0.9975,1,0.982778,0.998333,0.997222,0.998889,0.997778,1,0.996111]
area_under_roc_curve
0.997638888888889 [0.998889,0.999444,1,1,0.999722,0.989167,1,0.995278,0.998889,0.995]
area_under_roc_curve
0.9984722222222223 [0.999722,0.998611,1,0.997222,0.996389,0.997778,1,0.998889,0.999444,0.996667]
area_under_roc_curve
0.99625 [0.998611,0.995,1,0.991111,0.994722,0.988056,1,0.998611,0.997778,0.998611]
area_under_roc_curve
0.9980833333333335 [1,0.998611,1,1,0.996944,0.994722,0.9975,0.999722,0.998333,0.995]
area_under_roc_curve
0.9981111111111112 [1,0.996389,0.999444,0.999444,1,0.996111,1,0.999167,0.994722,0.995833]
area_under_roc_curve
0.9990277777777777 [1,0.999444,1,1,0.997778,0.999722,0.995833,1,0.9975,1]
area_under_roc_curve
0.9977777777777779 [0.999167,1,0.999444,0.9875,0.999444,0.995278,0.998333,1,0.998611,1]
area_under_roc_curve
0.9964166666666666 [1,0.998056,1,0.998889,0.997778,0.999444,0.976389,0.999722,0.995833,0.998056]
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
0.9595773204117234 [0.95,0.894737,1,0.97561,0.97561,0.95,0.974359,1,0.923077,0.952381]
f_measure
0.9401466517348536 [1,0.883721,1,0.9,0.888889,0.923077,0.930233,0.926829,0.974359,0.974359]
f_measure
0.9655213485701291 [0.974359,0.974359,1,0.974359,0.97561,0.923077,1,0.974359,0.95,0.909091]
f_measure
0.9550250378157356 [0.974359,0.930233,0.952381,0.918919,0.974359,0.95,1,0.95,0.95,0.95]
f_measure
0.9258610805979227 [0.952381,0.863636,0.974359,0.923077,0.894737,0.857143,1,0.974359,0.918919,0.9]
f_measure
0.9600785735445555 [1,0.97561,1,0.974359,0.95,0.904762,0.947368,0.97561,0.95,0.923077]
f_measure
0.9551584248810866 [0.974359,0.947368,0.974359,0.930233,0.97561,0.923077,1,0.952381,0.926829,0.947368]
f_measure
0.964579883468142 [0.952381,0.974359,0.97561,1,0.930233,0.974359,0.974359,1,0.888889,0.97561]
f_measure
0.945169654906497 [0.95,0.974359,0.95,0.904762,0.952381,0.923077,0.947368,0.952381,0.95,0.947368]
f_measure
0.9449633699633699 [0.974359,0.952381,1,0.95,0.974359,0.952381,0.871795,0.974359,0.9,0.9]
kappa
0.9555555555555555
kappa
0.9333333333333332
kappa
0.961111111111111
kappa
0.95
kappa
0.9166666666666667
kappa
0.9555555555555555
kappa
0.95
kappa
0.961111111111111
kappa
0.9388888888888889
kappa
0.9388888888888889
kb_relative_information_score
0.9521897532920002
kb_relative_information_score
0.9374785862854496
kb_relative_information_score
0.954095263578571
kb_relative_information_score
0.9536358311552362
kb_relative_information_score
0.9180622789417093
kb_relative_information_score
0.9529636657517753
kb_relative_information_score
0.9518386580029735
kb_relative_information_score
0.9597768520345628
kb_relative_information_score
0.9489516148813312
kb_relative_information_score
0.943855479248739
mean_absolute_error
0.011355099863017163
mean_absolute_error
0.015402299011114104
mean_absolute_error
0.01153836008283019
mean_absolute_error
0.010694936157503993
mean_absolute_error
0.021230070791644247
mean_absolute_error
0.010593334836606188
mean_absolute_error
0.011209666730440371
mean_absolute_error
0.010410650691101838
mean_absolute_error
0.012410126233058205
mean_absolute_error
0.012404264429815475
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
0.9605665679349891 [0.95,0.944444,1,0.952381,0.952381,0.95,1,1,0.947368,0.909091]
precision
0.944778249972758 [1,0.826087,1,0.9,1,0.947368,0.869565,0.904762,1,1]
precision
0.9683082706766918 [1,1,1,1,0.952381,0.947368,1,1,0.95,0.833333]
precision
0.9578656126482212 [1,0.869565,0.909091,1,1,0.95,1,0.95,0.95,0.95]
precision
0.931075225943647 [0.909091,0.791667,1,0.947368,0.944444,0.818182,1,1,1,0.9]
precision
0.96157666894509 [1,0.952381,1,1,0.95,0.863636,1,0.952381,0.95,0.947368]
precision
0.9583167404677703 [1,1,1,0.869565,0.952381,0.947368,1,0.909091,0.904762,1]
precision
0.9683418031244118 [0.909091,1,0.952381,1,0.869565,1,1,1,1,0.952381]
precision
0.9479186602870814 [0.95,1,0.95,0.863636,0.909091,0.947368,1,0.909091,0.95,1]
precision
0.9462918660287082 [1,0.909091,1,0.95,1,0.909091,0.894737,1,0.9,0.9]
predictive_accuracy
0.96
predictive_accuracy
0.94
predictive_accuracy
0.965
predictive_accuracy
0.955
predictive_accuracy
0.925
predictive_accuracy
0.96
predictive_accuracy
0.955
predictive_accuracy
0.965
predictive_accuracy
0.945
predictive_accuracy
0.945
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
0.0630838881278732
relative_absolute_error
0.0855683278395229
relative_absolute_error
0.0641020004601678
relative_absolute_error
0.05941631198613337
relative_absolute_error
0.11794483773135706
relative_absolute_error
0.05885186020336777
relative_absolute_error
0.06227592628022435
relative_absolute_error
0.05783694828389916
relative_absolute_error
0.06894514573921233
relative_absolute_error
0.0689125801656416
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
0.08609178543568423
root_mean_squared_error
0.09512595164002498
root_mean_squared_error
0.08416839794800114
root_mean_squared_error
0.08049326777992838
root_mean_squared_error
0.10506485628752883
root_mean_squared_error
0.08637119520476061
root_mean_squared_error
0.08454775372765388
root_mean_squared_error
0.07433711146184639
root_mean_squared_error
0.09031667635419985
root_mean_squared_error
0.0981163539549229
root_relative_squared_error
0.2869726181189476
root_relative_squared_error
0.31708650546675016
root_relative_squared_error
0.2805613264933373
root_relative_squared_error
0.2683108925997615
root_relative_squared_error
0.3502161876250963
root_relative_squared_error
0.28790398401586886
root_relative_squared_error
0.28182584575884645
root_relative_squared_error
0.24779037153948813
root_relative_squared_error
0.30105558784733305
root_relative_squared_error
0.32705451318307643
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
0.96 [0.95,0.85,1,1,1,0.95,0.95,1,0.9,1]
unweighted_recall
0.9400000000000001 [1,0.95,1,0.9,0.8,0.9,1,0.95,0.95,0.95]
unweighted_recall
0.9650000000000001 [0.95,0.95,1,0.95,1,0.9,1,0.95,0.95,1]
unweighted_recall
0.9549999999999998 [0.95,1,1,0.85,0.95,0.95,1,0.95,0.95,0.95]
unweighted_recall
0.925 [1,0.95,0.95,0.9,0.85,0.9,1,0.95,0.85,0.9]
unweighted_recall
0.9600000000000002 [1,1,1,0.95,0.95,0.95,0.9,1,0.95,0.9]
unweighted_recall
0.9550000000000001 [0.95,0.9,0.95,1,1,0.9,1,1,0.95,0.9]
unweighted_recall
0.9650000000000001 [1,0.95,1,1,1,0.95,0.95,1,0.8,1]
unweighted_recall
0.9450000000000001 [0.95,0.95,0.95,0.95,1,0.9,0.9,1,0.95,0.9]
unweighted_recall
0.9450000000000001 [0.95,1,1,0.95,0.95,1,0.85,0.95,0.9,0.9]
usercpu_time_millis
7252.209891000348
usercpu_time_millis
5147.134561999337
usercpu_time_millis
7339.891790000365
usercpu_time_millis
6420.927780000056
usercpu_time_millis
4541.327655999339
usercpu_time_millis
7068.9321879999625
usercpu_time_millis
7982.772502999978
usercpu_time_millis
6044.2999750002855
usercpu_time_millis
6387.452982999093
usercpu_time_millis
7604.426196001441
usercpu_time_millis_testing
2.9279000000315136
usercpu_time_millis_testing
2.775499999188469
usercpu_time_millis_testing
2.951800000118965
usercpu_time_millis_testing
3.4607000006872113
usercpu_time_millis_testing
3.3251999993808568
usercpu_time_millis_testing
2.811600000313774
usercpu_time_millis_testing
3.0029999998077983
usercpu_time_millis_testing
3.316100000120059
usercpu_time_millis_testing
3.4872009991886443
usercpu_time_millis_testing
2.932300000793475
usercpu_time_millis_training
7249.281991000316
usercpu_time_millis_training
5144.359062000149
usercpu_time_millis_training
7336.939990000246
usercpu_time_millis_training
6417.467079999369
usercpu_time_millis_training
4538.002455999958
usercpu_time_millis_training
7066.120587999649
usercpu_time_millis_training
7979.7695030001705
usercpu_time_millis_training
6040.983875000165
usercpu_time_millis_training
6383.965781999905
usercpu_time_millis_training
7601.4938960006475
wall_clock_time_millis
7258.0249309539795
wall_clock_time_millis
5157.337188720703
wall_clock_time_millis
7340.352296829224
wall_clock_time_millis
6429.428339004517
wall_clock_time_millis
4550.028324127197
wall_clock_time_millis
7082.944393157959
wall_clock_time_millis
7996.770858764648
wall_clock_time_millis
6049.736261367798
wall_clock_time_millis
6390.425205230713
wall_clock_time_millis
7621.835947036743
wall_clock_time_millis_testing
2.9327869415283203
wall_clock_time_millis_testing
2.7780532836914062
wall_clock_time_millis_testing
2.955198287963867
wall_clock_time_millis_testing
3.464221954345703
wall_clock_time_millis_testing
3.328084945678711
wall_clock_time_millis_testing
2.8150081634521484
wall_clock_time_millis_testing
3.0062198638916016
wall_clock_time_millis_testing
3.319978713989258
wall_clock_time_millis_testing
3.490447998046875
wall_clock_time_millis_testing
2.935647964477539
wall_clock_time_millis_training
7255.092144012451
wall_clock_time_millis_training
5154.559135437012
wall_clock_time_millis_training
7337.39709854126
wall_clock_time_millis_training
6425.964117050171
wall_clock_time_millis_training
4546.700239181519
wall_clock_time_millis_training
7080.129384994507
wall_clock_time_millis_training
7993.764638900757
wall_clock_time_millis_training
6046.416282653809
wall_clock_time_millis_training
6386.934757232666
wall_clock_time_millis_training
7618.900299072266
weighted_recall
0.96 [0.95,0.85,1,1,1,0.95,0.95,1,0.9,1]
weighted_recall
0.94 [1,0.95,1,0.9,0.8,0.9,1,0.95,0.95,0.95]
weighted_recall
0.965 [0.95,0.95,1,0.95,1,0.9,1,0.95,0.95,1]
weighted_recall
0.955 [0.95,1,1,0.85,0.95,0.95,1,0.95,0.95,0.95]
weighted_recall
0.925 [1,0.95,0.95,0.9,0.85,0.9,1,0.95,0.85,0.9]
weighted_recall
0.96 [1,1,1,0.95,0.95,0.95,0.9,1,0.95,0.9]
weighted_recall
0.955 [0.95,0.9,0.95,1,1,0.9,1,1,0.95,0.9]
weighted_recall
0.965 [1,0.95,1,1,1,0.95,0.95,1,0.8,1]
weighted_recall
0.945 [0.95,0.95,0.95,0.95,1,0.9,0.9,1,0.95,0.9]
weighted_recall
0.945 [0.95,1,1,0.95,0.95,1,0.85,0.95,0.9,0.9]