10576529
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)
8292811
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
0.9118320230516977
19038
loss
"deviance"
19038
max_depth
3
19038
max_features
null
19038
max_leaf_nodes
914
19038
min_impurity_decrease
0.0
19038
min_impurity_split
null
19038
min_samples_leaf
140
19038
min_samples_split
2
19038
min_weight_fraction_leaf
0.0
19038
n_estimators
100
19038
n_iter_no_change
11
19038
random_state
29961
19038
subsample
1.0
19038
tol
0.0001
19038
validation_fraction
0.08262384105539997
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
22079325
description
https://api.openml.org/data/download/22079325/description.xml
-1
22079326
predictions
https://api.openml.org/data/download/22079326/predictions.arff
area_under_roc_curve
0.8429338888888889 [0.846233,0.882911,0.884681,0.918889,0.965818,0.925433,0.963561,0.368822,0.923253,0.749737]
average_cost
0
f_measure
0.7500471049652975 [0.812665,0.710817,0.82963,0.775056,0.874036,0.811224,0.879177,0.377246,0.805333,0.625287]
kappa
0.726111111111111
kb_relative_information_score
0.7475909509122382
mean_absolute_error
0.051532362141681026
mean_prior_absolute_error
0.18000000000000554
weighted_recall
0.7535 [0.77,0.805,0.84,0.87,0.85,0.795,0.855,0.315,0.755,0.68]
number_of_instances
2000 [200,200,200,200,200,200,200,200,200,200]
precision
0.7559093812815816 [0.860335,0.636364,0.819512,0.698795,0.899471,0.828125,0.904762,0.470149,0.862857,0.578723]
predictive_accuracy
0.7535
prior_entropy
3.3219280948872383
relative_absolute_error
0.286290900787108
root_mean_prior_squared_error
0.3000000000000046
root_mean_squared_error
0.21244297746841714
root_relative_squared_error
0.7081432582280462
total_cost
0
unweighted_recall
0.7535000000000001 [0.77,0.805,0.84,0.87,0.85,0.795,0.855,0.315,0.755,0.68]
area_under_roc_curve
0.8813333333333334 [0.985556,0.839167,1,0.912222,0.988056,0.905278,0.998889,0.293611,0.945556,0.945]
area_under_roc_curve
0.7862916666666666 [0.55,0.805278,0.999444,0.921111,0.892222,0.792639,0.993333,0.528611,0.875,0.505278]
area_under_roc_curve
0.6813333333333332 [0.494722,0.789167,0.440833,0.96,0.899722,0.991111,0.835556,0.195556,0.715,0.491667]
area_under_roc_curve
0.816263888888889 [0.678056,0.939444,0.996667,0.970556,0.959722,0.906944,0.998333,0.153194,0.99,0.569722]
area_under_roc_curve
0.8603888888888889 [1,0.862222,0.996389,0.902778,0.981389,0.953889,0.970833,0.314722,0.975,0.646667]
area_under_roc_curve
0.9113055555555555 [1,0.997778,1,0.91,0.997778,0.982778,0.985556,0.301389,0.993889,0.943889]
area_under_roc_curve
0.9078055555555556 [0.996944,0.946667,0.995833,0.908056,0.999167,0.996667,0.999167,0.407222,0.978056,0.850278]
area_under_roc_curve
0.9084027777777777 [1,0.937361,0.998889,0.997222,0.999444,0.980278,0.997222,0.300278,0.959722,0.913611]
area_under_roc_curve
0.7676805555555555 [0.909167,0.790139,0.549444,0.7425,0.996944,0.785556,0.936667,0.293333,0.914167,0.758889]
area_under_roc_curve
0.9941666666666668 [1,0.997778,1,1,0.995833,0.999167,0.958611,1,0.993611,0.996667]
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
0.7458052549515963 [0.918919,0.545455,0.974359,0.666667,0.820513,0.820513,0.926829,0.214286,0.75,0.820513]
f_measure
0.6601649513639245 [0.486486,0.5,0.926829,0.64,0.736842,0.648649,0.923077,0.555556,0.684211,0.5]
f_measure
0.5787279801916025 [0.4375,0.764706,0.311111,0.947368,0.789474,0.810811,0.764706,0.070175,0.571429,0.32]
f_measure
0.7402383392029147 [0.615385,0.85,0.926829,0.974359,0.85,0.842105,0.926829,0.054054,0.894737,0.468085]
f_measure
0.7634424022056061 [1,0.580645,0.947368,0.842105,0.888889,0.734694,0.894737,0.344828,0.820513,0.580645]
f_measure
0.8345995959268789 [0.930233,0.926829,1,0.690909,0.926829,0.878049,0.888889,0.37037,0.923077,0.810811]
f_measure
0.8237260480315082 [0.857143,0.864865,0.926829,0.654545,0.947368,0.930233,0.904762,0.533333,0.818182,0.8]
f_measure
0.8048075843711275 [0.97561,0.761905,0.947368,0.9,0.909091,0.833333,0.926829,0.4,0.727273,0.666667]
f_measure
0.6552940117490202 [0.833333,0.576923,0.428571,0.666667,0.918919,0.666667,0.736842,0.32,0.888889,0.516129]
f_measure
0.9452268741100576 [0.974359,0.952381,1,0.909091,0.947368,0.923077,0.871795,1,0.926829,0.947368]
kappa
0.7222222222222222
kappa
0.6222222222222222
kappa
0.48888888888888893
kappa
0.7111111111111111
kappa
0.7388888888888889
kappa
0.8277777777777777
kappa
0.8055555555555555
kappa
0.7944444444444444
kappa
0.6111111111111112
kappa
0.9388888888888889
kb_relative_information_score
0.7520194237880231
kb_relative_information_score
0.6574061287195443
kb_relative_information_score
0.5236982497472971
kb_relative_information_score
0.733854776933442
kb_relative_information_score
0.7533082314857082
kb_relative_information_score
0.8424040700234946
kb_relative_information_score
0.8143328037291991
kb_relative_information_score
0.8086178189386863
kb_relative_information_score
0.641236880632985
kb_relative_information_score
0.9490311251237774
mean_absolute_error
0.050374538011306196
mean_absolute_error
0.06745303440258937
mean_absolute_error
0.09296996135740476
mean_absolute_error
0.05463024179175891
mean_absolute_error
0.04969777298197694
mean_absolute_error
0.035514167641663236
mean_absolute_error
0.042891892192479866
mean_absolute_error
0.0387296356257734
mean_absolute_error
0.07129801712954743
mean_absolute_error
0.011764360282306884
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
0.778303621958121 [1,0.428571,1,0.548387,0.842105,0.842105,0.904762,0.375,1,0.842105]
precision
0.6736829205366357 [0.529412,0.428571,0.904762,0.533333,0.777778,0.705882,0.947368,0.625,0.722222,0.5625]
precision
0.6423549852373381 [0.583333,0.928571,0.28,1,0.833333,0.882353,0.928571,0.054054,0.666667,0.266667]
precision
0.7440667027044736 [0.631579,0.85,0.904762,1,0.85,0.888889,0.904762,0.058824,0.944444,0.407407]
precision
0.8098437053972444 [1,0.428571,1,0.888889,1,0.62069,0.944444,0.555556,0.842105,0.818182]
precision
0.862309610342993 [0.869565,0.904762,1,0.542857,0.904762,0.857143,1,0.714286,0.947368,0.882353]
precision
0.8576759003996856 [1,0.941176,0.904762,0.514286,1,0.869565,0.863636,0.8,0.75,0.933333]
precision
0.867832584082584 [0.952381,0.727273,1,0.9,0.833333,0.9375,0.904762,1,0.923077,0.5]
precision
0.7304435440290703 [0.9375,0.46875,0.409091,0.684211,1,0.846154,0.777778,0.8,1,0.380952]
precision
0.9489291410344042 [1,0.909091,1,0.833333,1,0.947368,0.894737,1,0.904762,1]
predictive_accuracy
0.75
predictive_accuracy
0.66
predictive_accuracy
0.54
predictive_accuracy
0.74
predictive_accuracy
0.765
predictive_accuracy
0.845
predictive_accuracy
0.825
predictive_accuracy
0.815
predictive_accuracy
0.65
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
0.27985854450725695
relative_absolute_error
0.37473908001438583
relative_absolute_error
0.5164997853189159
relative_absolute_error
0.30350134328754985
relative_absolute_error
0.27609873878876107
relative_absolute_error
0.19730093134257376
relative_absolute_error
0.23828828995822174
relative_absolute_error
0.21516464236540803
relative_absolute_error
0.3961000951641529
relative_absolute_error
0.06535755712392721
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
0.21049237075654864
root_mean_squared_error
0.2508669507607512
root_mean_squared_error
0.2981960381195091
root_mean_squared_error
0.21885115818633674
root_mean_squared_error
0.2094735123335237
root_mean_squared_error
0.1631417499329979
root_mean_squared_error
0.1756174393916365
root_mean_squared_error
0.18024855849988058
root_mean_squared_error
0.2550393993645305
root_mean_squared_error
0.0916062716605889
root_relative_squared_error
0.7016412358551627
root_relative_squared_error
0.8362231692025045
root_relative_squared_error
0.9939867937316977
root_relative_squared_error
0.729503860621123
root_relative_squared_error
0.6982450411117462
root_relative_squared_error
0.5438058331099933
root_relative_squared_error
0.5853914646387887
root_relative_squared_error
0.6008285283329355
root_relative_squared_error
0.8501313312151022
root_relative_squared_error
0.3053542388686299
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
0.75 [0.85,0.75,0.95,0.85,0.8,0.8,0.95,0.15,0.6,0.8]
unweighted_recall
0.66 [0.45,0.6,0.95,0.8,0.7,0.6,0.9,0.5,0.65,0.45]
unweighted_recall
0.54 [0.35,0.65,0.35,0.9,0.75,0.75,0.65,0.1,0.5,0.4]
unweighted_recall
0.7399999999999999 [0.6,0.85,0.95,0.95,0.85,0.8,0.95,0.05,0.85,0.55]
unweighted_recall
0.7649999999999999 [1,0.9,0.9,0.8,0.8,0.9,0.85,0.25,0.8,0.45]
unweighted_recall
0.8450000000000001 [1,0.95,1,0.95,0.95,0.9,0.8,0.25,0.9,0.75]
unweighted_recall
0.825 [0.75,0.8,0.95,0.9,0.9,1,0.95,0.4,0.9,0.7]
unweighted_recall
0.8149999999999998 [1,0.8,0.9,0.9,1,0.75,0.95,0.25,0.6,1]
unweighted_recall
0.65 [0.75,0.75,0.45,0.65,0.85,0.55,0.7,0.2,0.8,0.8]
unweighted_recall
0.9450000000000001 [0.95,1,1,1,0.9,0.9,0.85,1,0.95,0.9]
usercpu_time_millis
3947.0656490011606
usercpu_time_millis
3898.215350999635
usercpu_time_millis
2557.544734001567
usercpu_time_millis
2603.803436999442
usercpu_time_millis
3898.4377479991963
usercpu_time_millis
2598.8992340007826
usercpu_time_millis
2593.7771310000244
usercpu_time_millis
2557.6985320003587
usercpu_time_millis
2748.53373499991
usercpu_time_millis
5258.079264998742
usercpu_time_millis_testing
2.8154000010545133
usercpu_time_millis_testing
2.8926009999850066
usercpu_time_millis_testing
3.033500001038192
usercpu_time_millis_testing
3.3910000001924345
usercpu_time_millis_testing
2.284199999849079
usercpu_time_millis_testing
2.980100000058883
usercpu_time_millis_testing
3.444900999966194
usercpu_time_millis_testing
3.0977999995229766
usercpu_time_millis_testing
2.7550009999686154
usercpu_time_millis_testing
2.669699999387376
usercpu_time_millis_training
3944.250249000106
usercpu_time_millis_training
3895.32274999965
usercpu_time_millis_training
2554.511234000529
usercpu_time_millis_training
2600.4124369992496
usercpu_time_millis_training
3896.153547999347
usercpu_time_millis_training
2595.9191340007237
usercpu_time_millis_training
2590.332230000058
usercpu_time_millis_training
2554.6007320008357
usercpu_time_millis_training
2745.7787339999413
usercpu_time_millis_training
5255.409564999354
wall_clock_time_millis
3950.6704807281494
wall_clock_time_millis
3899.702310562134
wall_clock_time_millis
2558.8226318359375
wall_clock_time_millis
2617.755889892578
wall_clock_time_millis
3901.1409282684326
wall_clock_time_millis
2601.1691093444824
wall_clock_time_millis
2600.8996963500977
wall_clock_time_millis
2560.610294342041
wall_clock_time_millis
2751.5788078308105
wall_clock_time_millis
5266.014575958252
wall_clock_time_millis_testing
2.819061279296875
wall_clock_time_millis_testing
2.897024154663086
wall_clock_time_millis_testing
3.039121627807617
wall_clock_time_millis_testing
3.3965110778808594
wall_clock_time_millis_testing
2.2878646850585938
wall_clock_time_millis_testing
2.9823780059814453
wall_clock_time_millis_testing
3.451108932495117
wall_clock_time_millis_testing
3.1027793884277344
wall_clock_time_millis_testing
2.760648727416992
wall_clock_time_millis_testing
2.672433853149414
wall_clock_time_millis_training
3947.8514194488525
wall_clock_time_millis_training
3896.8052864074707
wall_clock_time_millis_training
2555.78351020813
wall_clock_time_millis_training
2614.3593788146973
wall_clock_time_millis_training
3898.853063583374
wall_clock_time_millis_training
2598.186731338501
wall_clock_time_millis_training
2597.4485874176025
wall_clock_time_millis_training
2557.5075149536133
wall_clock_time_millis_training
2748.8181591033936
wall_clock_time_millis_training
5263.3421421051025
weighted_recall
0.75 [0.85,0.75,0.95,0.85,0.8,0.8,0.95,0.15,0.6,0.8]
weighted_recall
0.66 [0.45,0.6,0.95,0.8,0.7,0.6,0.9,0.5,0.65,0.45]
weighted_recall
0.54 [0.35,0.65,0.35,0.9,0.75,0.75,0.65,0.1,0.5,0.4]
weighted_recall
0.74 [0.6,0.85,0.95,0.95,0.85,0.8,0.95,0.05,0.85,0.55]
weighted_recall
0.765 [1,0.9,0.9,0.8,0.8,0.9,0.85,0.25,0.8,0.45]
weighted_recall
0.845 [1,0.95,1,0.95,0.95,0.9,0.8,0.25,0.9,0.75]
weighted_recall
0.825 [0.75,0.8,0.95,0.9,0.9,1,0.95,0.4,0.9,0.7]
weighted_recall
0.815 [1,0.8,0.9,0.9,1,0.75,0.95,0.25,0.6,1]
weighted_recall
0.65 [0.75,0.75,0.45,0.65,0.85,0.55,0.7,0.2,0.8,0.8]
weighted_recall
0.945 [0.95,1,1,1,0.9,0.9,0.85,1,0.95,0.9]