10437785
11497
Fares Gaaloul
2275
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.
278
meta_instanceincremental.arff
https://www.openml.org/data/download/12456/meta_instanceincremental.arff
-1
21799923
description
https://api.openml.org/data/download/21799923/description.xml
-1
21799924
predictions
https://api.openml.org/data/download/21799924/predictions.arff
area_under_roc_curve
0.9189535134957669 [0.870265,0.910648,0.989744,0.985915]
average_cost
0
f_measure
0.8544657511762775 [0.461538,0.912281,0.875,0.8]
kappa
0.6520921485660556
kb_relative_information_score
-0.4430532619480587
mean_absolute_error
0.35449692181253706
mean_prior_absolute_error
0.22765072765072786
weighted_recall
0.8648648648648649 [0.375,0.962963,0.777778,0.666667]
number_of_instances
74 [8,54,9,3]
precision
0.8594594594594595 [0.6,0.866667,1,1]
predictive_accuracy
0.8648648648648648
prior_entropy
1.238778199999325
relative_absolute_error
1.5571965241263026
root_mean_prior_squared_error
0.3317335939498282
root_mean_squared_error
0.4095752942249476
root_relative_squared_error
1.2346512433313952
total_cost
0
unweighted_recall
0.6956018518518517 [0.375,0.962963,0.777778,0.666667]
area_under_roc_curve
1 [1,1,1,0.0]
area_under_roc_curve
0.9196428571428571 [0.0,0.916667,1,0.857143]
area_under_roc_curve
1 [0.0,1,1,1]
area_under_roc_curve
0.9107142857142857 [0.785714,0.9,1,1]
area_under_roc_curve
0.9523809523809524 [0.666667,1,1,0.0]
area_under_roc_curve
1 [1,1,1,0.0]
area_under_roc_curve
1 [1,1,1,0.0]
area_under_roc_curve
1 [1,1,1,0.0]
area_under_roc_curve
0.9047619047619048 [0.833333,0.9,1,0.0]
area_under_roc_curve
0.8333333333333333 [0.833333,0.833333,0.0,0.0]
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
average_cost
0
f_measure
1 [1,1,1,0.0]
f_measure
1 [0.0,1,1,1]
f_measure
1 [1,1,1,0.0]
f_measure
0.7142857142857144 [0,0.8,1,0.0]
f_measure
0.7142857142857143 [0,0.833333,0.0,0.0]
kappa
1
kappa
0.6190476190476191
kappa
1
kappa
0.75
kappa
0
kappa
0.6111111111111109
kappa
1
kappa
0.6111111111111109
kappa
0.36363636363636376
kappa
-0.16666666666666635
kb_relative_information_score
-0.5488852188592569
kb_relative_information_score
-0.38703379455618764
kb_relative_information_score
-0.38845548395000434
kb_relative_information_score
-0.11693852287463628
kb_relative_information_score
-0.4958740352697671
kb_relative_information_score
-0.4522118662408287
kb_relative_information_score
-0.43607446682250184
kb_relative_information_score
-0.4651872526700883
kb_relative_information_score
-0.4555164233196725
kb_relative_information_score
-1.0301076331724714
mean_absolute_error
0.35211746019713785
mean_absolute_error
0.3521136072197948
mean_absolute_error
0.3547296943302829
mean_absolute_error
0.3537316468655298
mean_absolute_error
0.35974716020192105
mean_absolute_error
0.3534816800315678
mean_absolute_error
0.353595935283621
mean_absolute_error
0.3571805540474844
mean_absolute_error
0.3554648347311182
mean_absolute_error
0.3535631135936864
mean_prior_absolute_error
0.2203525641025641
mean_prior_absolute_error
0.22435897435897434
mean_prior_absolute_error
0.22435897435897434
mean_prior_absolute_error
0.26121794871794873
mean_prior_absolute_error
0.23076923076923078
mean_prior_absolute_error
0.23076923076923078
mean_prior_absolute_error
0.23076923076923078
mean_prior_absolute_error
0.23076923076923078
mean_prior_absolute_error
0.23076923076923078
mean_prior_absolute_error
0.18956043956043958
number_of_instances
8 [1,6,1,0]
number_of_instances
8 [0,6,1,1]
number_of_instances
8 [0,6,1,1]
number_of_instances
8 [1,5,1,1]
number_of_instances
7 [1,5,1,0]
number_of_instances
7 [1,5,1,0]
number_of_instances
7 [1,5,1,0]
number_of_instances
7 [1,5,1,0]
number_of_instances
7 [1,5,1,0]
number_of_instances
7 [1,6,0,0]
precision
1 [1,1,1,0.0]
precision
1 [0.0,1,1,1]
precision
1 [1,1,1,0.0]
precision
0.7142857142857143 [0,0.8,1,0.0]
precision
0.7142857142857143 [0,0.833333,0.0,0.0]
predictive_accuracy
1
predictive_accuracy
0.875
predictive_accuracy
1
predictive_accuracy
0.875
predictive_accuracy
0.7142857142857143
predictive_accuracy
0.8571428571428571
predictive_accuracy
1
predictive_accuracy
0.8571428571428571
predictive_accuracy
0.7142857142857143
predictive_accuracy
0.7142857142857143
prior_entropy
1.1379007966775443
prior_entropy
1.2841414218578335
prior_entropy
1.2841414218578335
prior_entropy
1.6105707608681268
prior_entropy
1.2284519811546808
prior_entropy
1.2284519811546808
prior_entropy
1.2284519811546808
prior_entropy
1.2284519811546808
prior_entropy
1.2284519811546808
prior_entropy
0.8771046070636382
relative_absolute_error
1.5979730557310112
relative_absolute_error
1.569420649322514
relative_absolute_error
1.5810809233006895
relative_absolute_error
1.3541628689821508
relative_absolute_error
1.5589043608749913
relative_absolute_error
1.5317539468034604
relative_absolute_error
1.5322490528956907
relative_absolute_error
1.5477824008724324
relative_absolute_error
1.540347617168179
relative_absolute_error
1.8651735267840845
root_mean_prior_squared_error
0.32054487019246797
root_mean_prior_squared_error
0.3267344855737092
root_mean_prior_squared_error
0.3267344855737092
root_mean_prior_squared_error
0.3789649039450629
root_mean_prior_squared_error
0.33640107085645965
root_mean_prior_squared_error
0.33640107085645965
root_mean_prior_squared_error
0.33640107085645965
root_mean_prior_squared_error
0.33640107085645965
root_mean_prior_squared_error
0.33640107085645965
root_mean_prior_squared_error
0.26824781315899215
root_mean_squared_error
0.4066278166255955
root_mean_squared_error
0.4069350389753289
root_mean_squared_error
0.4096765032719036
root_mean_squared_error
0.408949218848967
root_mean_squared_error
0.41574058542614045
root_mean_squared_error
0.4084002717369861
root_mean_squared_error
0.40836557637911014
root_mean_squared_error
0.412642420277675
root_mean_squared_error
0.41068535532470535
root_mean_squared_error
0.40851674516614644
root_relative_squared_error
1.2685519390200815
root_relative_squared_error
1.2454609382930502
root_relative_squared_error
1.25385143399405
root_relative_squared_error
1.0791216141435906
root_relative_squared_error
1.235847984572958
root_relative_squared_error
1.2140278587616273
root_relative_squared_error
1.2139247218786569
root_relative_squared_error
1.2266382482882972
root_relative_squared_error
1.2208205945335482
root_relative_squared_error
1.5229080168643014
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 [0,1,1,1]
usercpu_time_millis
383.3196590000014
usercpu_time_millis
95.18873399999705
usercpu_time_millis
741.9232870000022
usercpu_time_millis
11.594927000000865
usercpu_time_millis
84.75057199999725
usercpu_time_millis
139.29700799999978
usercpu_time_millis
117.16133599999878
usercpu_time_millis
236.36414099999925
usercpu_time_millis
753.5183879999962
usercpu_time_millis
87.30232999999998
usercpu_time_millis_testing
11.207319000000382
usercpu_time_millis_testing
3.3260149999989608
usercpu_time_millis_testing
21.517560999999574
usercpu_time_millis_testing
0.8917450000005545
usercpu_time_millis_testing
3.0464359999982094
usercpu_time_millis_testing
4.190922999999458
usercpu_time_millis_testing
3.9945119999984513
usercpu_time_millis_testing
7.17024700000124
usercpu_time_millis_testing
22.195238999998423
usercpu_time_millis_testing
3.108826999998371
usercpu_time_millis_training
372.11234000000104
usercpu_time_millis_training
91.8627189999981
usercpu_time_millis_training
720.4057260000027
usercpu_time_millis_training
10.70318200000031
usercpu_time_millis_training
81.70413599999904
usercpu_time_millis_training
135.10608500000032
usercpu_time_millis_training
113.16682400000033
usercpu_time_millis_training
229.193893999998
usercpu_time_millis_training
731.3231489999978
usercpu_time_millis_training
84.19350300000161
wall_clock_time_millis
383.5015296936035
wall_clock_time_millis
95.54815292358398
wall_clock_time_millis
742.0673370361328
wall_clock_time_millis
11.262178421020508
wall_clock_time_millis
84.48028564453125
wall_clock_time_millis
138.54503631591797
wall_clock_time_millis
117.08831787109375
wall_clock_time_millis
235.3382110595703
wall_clock_time_millis
755.0761699676514
wall_clock_time_millis
87.56089210510254
wall_clock_time_millis_testing
11.214971542358398
wall_clock_time_millis_testing
3.329753875732422
wall_clock_time_millis_testing
21.629810333251953
wall_clock_time_millis_testing
0.8943080902099609
wall_clock_time_millis_testing
3.050088882446289
wall_clock_time_millis_testing
4.195451736450195
wall_clock_time_millis_testing
3.9992332458496094
wall_clock_time_millis_testing
7.177591323852539
wall_clock_time_millis_testing
22.478580474853516
wall_clock_time_millis_testing
3.11279296875
wall_clock_time_millis_training
372.2865581512451
wall_clock_time_millis_training
92.21839904785156
wall_clock_time_millis_training
720.4375267028809
wall_clock_time_millis_training
10.367870330810547
wall_clock_time_millis_training
81.43019676208496
wall_clock_time_millis_training
134.34958457946777
wall_clock_time_millis_training
113.08908462524414
wall_clock_time_millis_training
228.16061973571777
wall_clock_time_millis_training
732.5975894927979
wall_clock_time_millis_training
84.44809913635254
weighted_recall
1 [1,1,1,0.0]
weighted_recall
0.875 [0.0,1,1,0]
weighted_recall
1 [0.0,1,1,1]
weighted_recall
0.875 [0,1,1,1]
weighted_recall
0.7142857142857143 [0,1,0,0.0]
weighted_recall
0.8571428571428571 [0,1,1,0.0]
weighted_recall
1 [1,1,1,0.0]
weighted_recall
0.8571428571428571 [1,1,0,0.0]
weighted_recall
0.7142857142857143 [0,0.8,1,0.0]
weighted_recall
0.7142857142857143 [0,0.833333,0.0,0.0]