10437782
11497
Fares Gaaloul
2272
Supervised Classification
predictive_accuracy
17579
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),adaboostclassifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(2)
8260895
memory
null
17579
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "adaboostclassifier", "step_name": "adaboostclassifier"}}]
17579
n_jobs
null
17580
remainder
"passthrough"
17580
sparse_threshold
0.3
17580
transformer_weights
null
17580
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]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}]
17580
memory
null
17581
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "missingindicator", "step_name": "missingindicator"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
17581
error_on_new
false
17582
features
"missing-only"
17582
missing_values
NaN
17582
sparse
"auto"
17582
axis
0
17583
copy
true
17583
missing_values
"NaN"
17583
strategy
"median"
17583
verbose
0
17583
copy
true
17584
with_mean
true
17584
with_std
true
17584
memory
null
17585
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"}}]
17585
copy
true
17586
fill_value
-1
17586
missing_values
NaN
17586
strategy
"constant"
17586
verbose
0
17586
categorical_features
null
17587
categories
null
17587
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
17587
handle_unknown
"ignore"
17587
n_values
null
17587
sparse
true
17587
algorithm
"SAMME"
17588
learning_rate
0.8258296169139974
17588
n_estimators
429
17588
random_state
53944
17588
class_weight
null
17589
criterion
"gini"
17589
max_depth
3
17589
max_features
null
17589
max_leaf_nodes
null
17589
min_impurity_decrease
0.0
17589
min_impurity_split
null
17589
min_samples_leaf
1
17589
min_samples_split
2
17589
min_weight_fraction_leaf
0.0
17589
presort
false
17589
random_state
16546
17589
splitter
"best"
17589
openml-python
Sklearn_0.20.0.
275
meta_all.arff
https://www.openml.org/data/download/12453/meta_all.arff
-1
21799917
description
https://api.openml.org/data/download/21799917/description.xml
-1
21799918
predictions
https://api.openml.org/data/download/21799918/predictions.arff
area_under_roc_curve
0.7611968937389344 [1,1,0.835821,0.762726,0.790909,0.596721]
average_cost
0
f_measure
0.6330690022487869 [0.8,1,0.285714,0.8,0.470588,0.142857]
kappa
0.39225902493487164
kb_relative_information_score
-0.15265145713998649
mean_absolute_error
0.27530023542989784
mean_prior_absolute_error
0.20651179806109396
weighted_recall
0.676056338028169 [1,1,0.25,0.904762,0.363636,0.1]
number_of_instances
71 [2,2,4,42,11,10]
precision
0.6283550358756312 [0.666667,1,0.333333,0.716981,0.666667,0.25]
predictive_accuracy
0.676056338028169
prior_entropy
1.7941163185226785
relative_absolute_error
1.3330968884811787
root_mean_prior_squared_error
0.31698470376062077
root_mean_squared_error
0.3693780721912992
root_relative_squared_error
1.1652867403666414
total_cost
0
unweighted_recall
0.6030663780663781 [1,1,0.25,0.904762,0.363636,0.1]
area_under_roc_curve
0.75 [0.0,1,0.0,0.8,0.857143,0.142857]
area_under_roc_curve
0.4523809523809524 [0.0,0.0,0.0,0.3,0.666667,1]
area_under_roc_curve
0.5952380952380952 [0.0,0.0,0.0,0.416667,1,0.5]
area_under_roc_curve
0.7857142857142857 [0.0,0.0,0.666667,0.916667,1,0.166667]
area_under_roc_curve
0.9047619047619048 [0.0,0.0,1,0.916667,1,0.666667]
area_under_roc_curve
0.7142857142857144 [0.0,0.0,1,0.583333,1,0.666667]
area_under_roc_curve
0.8571428571428571 [0.0,0.0,1,0.75,1,1]
area_under_roc_curve
0.9523809523809524 [1,0.0,0.0,1,0.666667,1]
area_under_roc_curve
0.7142857142857143 [1,0.0,0.0,0.666667,0.666667,0.666667]
area_under_roc_curve
0.7142857142857143 [0.0,1,0.0,0.75,0.166667,0.833333]
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
0.5151515151515151
kappa
0
kappa
0.22222222222222218
kappa
0.5333333333333333
kappa
0.7407407407407407
kappa
-0.16666666666666674
kappa
0.4166666666666667
kappa
0.7407407407407407
kappa
0.22222222222222218
kappa
0.3225806451612902
kb_relative_information_score
-0.13734036062455263
kb_relative_information_score
-0.4246170232256318
kb_relative_information_score
-0.2667065284313127
kb_relative_information_score
-0.14601871199970037
kb_relative_information_score
-0.13495523163209638
kb_relative_information_score
-0.1428392759756045
kb_relative_information_score
-0.13986178354932008
kb_relative_information_score
-0.0726508434732036
kb_relative_information_score
-0.07740165372537769
kb_relative_information_score
-0.07657026268820118
mean_absolute_error
0.27539921420933944
mean_absolute_error
0.2752281628833792
mean_absolute_error
0.27578529029185933
mean_absolute_error
0.27571442355555514
mean_absolute_error
0.27454295257773476
mean_absolute_error
0.27566431968472077
mean_absolute_error
0.2753501929525122
mean_absolute_error
0.27466693889874655
mean_absolute_error
0.27532072516807293
mean_absolute_error
0.27531599425142317
mean_prior_absolute_error
0.20292207792207795
mean_prior_absolute_error
0.18614718614718617
mean_prior_absolute_error
0.20531849103277675
mean_prior_absolute_error
0.20964749536178107
mean_prior_absolute_error
0.20964749536178107
mean_prior_absolute_error
0.20964749536178107
mean_prior_absolute_error
0.20964749536178107
mean_prior_absolute_error
0.2108843537414966
mean_prior_absolute_error
0.2108843537414966
mean_prior_absolute_error
0.2108843537414966
number_of_instances
8 [0,1,0,5,1,1]
number_of_instances
7 [0,0,0,5,1,1]
number_of_instances
7 [0,0,0,4,2,1]
number_of_instances
7 [0,0,1,4,1,1]
number_of_instances
7 [0,0,1,4,1,1]
number_of_instances
7 [0,0,1,4,1,1]
number_of_instances
7 [0,0,1,4,1,1]
number_of_instances
7 [1,0,0,4,1,1]
number_of_instances
7 [1,0,0,4,1,1]
number_of_instances
7 [0,1,0,4,1,1]
predictive_accuracy
0.75
predictive_accuracy
0.7142857142857143
predictive_accuracy
0.5714285714285715
predictive_accuracy
0.7142857142857143
predictive_accuracy
0.8571428571428571
predictive_accuracy
0.42857142857142855
predictive_accuracy
0.7142857142857143
predictive_accuracy
0.8571428571428571
predictive_accuracy
0.5714285714285715
predictive_accuracy
0.5714285714285715
prior_entropy
1.796701491496139
prior_entropy
1.3845411274279098
prior_entropy
1.6475843065680442
prior_entropy
1.8280177931157293
prior_entropy
1.8280177931157293
prior_entropy
1.8280177931157293
prior_entropy
1.8280177931157293
prior_entropy
1.93329859228233
prior_entropy
1.93329859228233
prior_entropy
1.93329859228233
relative_absolute_error
1.3571673276236245
relative_absolute_error
1.478551293629316
relative_absolute_error
1.3432072722949897
relative_absolute_error
1.315133400853489
relative_absolute_error
1.3095455879592834
relative_absolute_error
1.3148944098235795
relative_absolute_error
1.3133960531097706
relative_absolute_error
1.3024529038101853
relative_absolute_error
1.3055531161195717
relative_absolute_error
1.3055306824180388
root_mean_prior_squared_error
0.31127091460525613
root_mean_prior_squared_error
0.2830453859441996
root_mean_prior_squared_error
0.31509680320481115
root_mean_prior_squared_error
0.3218928388748278
root_mean_prior_squared_error
0.3218928388748278
root_mean_prior_squared_error
0.3218928388748278
root_mean_prior_squared_error
0.3218928388748278
root_mean_prior_squared_error
0.32380836631966653
root_mean_prior_squared_error
0.32380836631966653
root_mean_prior_squared_error
0.32380836631966653
root_mean_squared_error
0.3695102053983379
root_mean_squared_error
0.3692804372748662
root_mean_squared_error
0.3700405196702173
root_mean_squared_error
0.3699382081421011
root_mean_squared_error
0.36835616281131034
root_mean_squared_error
0.36986772919048805
root_mean_squared_error
0.3694379881108707
root_mean_squared_error
0.3685215682740506
root_mean_squared_error
0.3694038854492122
root_mean_squared_error
0.3694013451265284
root_relative_squared_error
1.1871016148969107
root_relative_squared_error
1.3046686348304126
root_relative_squared_error
1.174370910483954
root_relative_squared_error
1.1492588944669142
root_relative_squared_error
1.144344074564984
root_relative_squared_error
1.1490399428684275
root_relative_squared_error
1.1477048989416365
root_relative_squared_error
1.1380853819886878
root_relative_squared_error
1.1408101947697464
root_relative_squared_error
1.1408023496275326
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
739.4951760000001
usercpu_time_millis
757.5445279999994
usercpu_time_millis
734.5652809999991
usercpu_time_millis
742.8959049999992
usercpu_time_millis
767.8657239999991
usercpu_time_millis
769.6143490000002
usercpu_time_millis
767.7174980000015
usercpu_time_millis
754.9397080000002
usercpu_time_millis
740.5854470000008
usercpu_time_millis
763.1127530000015
usercpu_time_millis_testing
21.17715099999984
usercpu_time_millis_testing
21.612088999999557
usercpu_time_millis_testing
20.82903899999966
usercpu_time_millis_testing
22.668885999999944
usercpu_time_millis_testing
22.19746199999939
usercpu_time_millis_testing
22.21639000000053
usercpu_time_millis_testing
22.81587200000068
usercpu_time_millis_testing
20.98108400000065
usercpu_time_millis_testing
21.805052999999575
usercpu_time_millis_testing
21.468241000000887
usercpu_time_millis_training
718.3180250000003
usercpu_time_millis_training
735.9324389999999
usercpu_time_millis_training
713.7362419999995
usercpu_time_millis_training
720.2270189999992
usercpu_time_millis_training
745.6682619999997
usercpu_time_millis_training
747.3979589999997
usercpu_time_millis_training
744.9016260000008
usercpu_time_millis_training
733.9586239999995
usercpu_time_millis_training
718.7803940000013
usercpu_time_millis_training
741.6445120000005
wall_clock_time_millis
742.0852184295654
wall_clock_time_millis
759.9906921386719
wall_clock_time_millis
735.4154586791992
wall_clock_time_millis
744.0676689147949
wall_clock_time_millis
774.5451927185059
wall_clock_time_millis
777.0376205444336
wall_clock_time_millis
773.5564708709717
wall_clock_time_millis
758.946418762207
wall_clock_time_millis
741.8041229248047
wall_clock_time_millis
768.5728073120117
wall_clock_time_millis_testing
20.994186401367188
wall_clock_time_millis_testing
21.71945571899414
wall_clock_time_millis_testing
20.837068557739258
wall_clock_time_millis_testing
22.876262664794922
wall_clock_time_millis_testing
22.539377212524414
wall_clock_time_millis_testing
22.340774536132812
wall_clock_time_millis_testing
23.081541061401367
wall_clock_time_millis_testing
20.98846435546875
wall_clock_time_millis_testing
21.904468536376953
wall_clock_time_millis_testing
21.544694900512695
wall_clock_time_millis_training
721.0910320281982
wall_clock_time_millis_training
738.2712364196777
wall_clock_time_millis_training
714.57839012146
wall_clock_time_millis_training
721.19140625
wall_clock_time_millis_training
752.0058155059814
wall_clock_time_millis_training
754.6968460083008
wall_clock_time_millis_training
750.4749298095703
wall_clock_time_millis_training
737.9579544067383
wall_clock_time_millis_training
719.8996543884277
wall_clock_time_millis_training
747.028112411499
weighted_recall
0.75 [0.0,1,0.0,1,0,0]
weighted_recall
0.7142857142857143 [0.0,0.0,0.0,1,0,0]
weighted_recall
0.5714285714285714 [0.0,0.0,0.0,0.75,0.5,0]
weighted_recall
0.7142857142857143 [0.0,0.0,0,1,1,0]
weighted_recall
0.8571428571428571 [0.0,0.0,1,1,1,0]
weighted_recall
0.42857142857142855 [0.0,0.0,0,0.75,0,0]
weighted_recall
0.7142857142857143 [0.0,0.0,0,1,1,0]
weighted_recall
0.8571428571428571 [1,0.0,0.0,1,0,1]
weighted_recall
0.5714285714285714 [1,0.0,0.0,0.75,0,0]
weighted_recall
0.5714285714285714 [0.0,1,0.0,0.75,0,0]