10591714
32911
tharu vk
5
Supervised Classification
predictive_accuracy
18983
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(2)
8304487
Python_3.7.3. Sklearn_0.20.0. NumPy_1.21.5. SciPy_1.7.3.
n_jobs
null
18952
remainder
"passthrough"
18952
sparse_threshold
0.3
18952
transformer_weights
null
18952
transformers
[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 27, 28, 29, 30, 31, 32, 39, 40, 41, 42, 43, 44, 51, 52, 53, 54, 55, 56, 63, 64, 65, 66, 67, 68, 75, 76, 77, 78, 79, 80, 87, 88, 89, 90, 91, 92, 99, 100, 101, 102, 103, 104, 111, 112, 113, 114, 115, 116, 123, 124, 125, 126, 127, 128, 135, 136, 137, 138, 139, 140, 147, 148, 149, 150, 151, 152, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": [1, 21, 22, 23, 24, 25, 26, 33, 34, 35, 36, 37, 38, 45, 46, 47, 48, 49, 50, 57, 58, 59, 60, 61, 62, 69, 70, 71, 72, 73, 74, 81, 82, 83, 84, 85, 86, 93, 94, 95, 96, 97, 98, 105, 106, 107, 108, 109, 110, 117, 118, 119, 120, 121, 122, 129, 130, 131, 132, 133, 134, 141, 142, 143, 144, 145, 146, 153, 154, 155, 156, 157, 158]}}]
18952
memory
null
18953
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
18953
axis
0
18954
copy
true
18954
missing_values
"NaN"
18954
strategy
"mean"
18954
verbose
0
18954
copy
true
18955
with_mean
true
18955
with_std
true
18955
memory
null
18956
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"}}]
18956
copy
true
18957
fill_value
-1
18957
missing_values
NaN
18957
strategy
"constant"
18957
verbose
0
18957
categorical_features
null
18958
categories
null
18958
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
18958
handle_unknown
"ignore"
18958
n_values
null
18958
sparse
true
18958
threshold
0.0
18959
class_weight
null
18971
criterion
"gini"
18971
max_depth
null
18971
max_features
1.0
18971
max_leaf_nodes
null
18971
min_impurity_decrease
0.0
18971
min_impurity_split
null
18971
min_samples_leaf
5
18971
min_samples_split
16
18971
min_weight_fraction_leaf
0.0
18971
presort
false
18971
random_state
0
18971
splitter
"best"
18971
memory
null
18983
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
18983
openml-python
Sklearn_0.20.0.
5
arrhythmia
https://www.openml.org/data/download/5/dataset_5_arrhythmia.arff
-1
22111561
description
https://api.openml.org/data/download/22111561/description.xml
-1
22111562
predictions
https://api.openml.org/data/download/22111562/predictions.arff
area_under_roc_curve
0.8111094068709102 [0.837711,0.780554,0.987796,0.852174,0.696163,0.906136,0.469933,0.478889,0.885001,0.81199,0.0,0.0,0.0,0.460938,0.454139,0.576744]
average_cost
0
kappa
0.5092993976312357
kb_relative_information_score
0.4990053402756713
mean_absolute_error
0.0461099180747964
mean_prior_absolute_error
0.08554099538612797
weighted_recall
0.6814159292035398 [0.840816,0.454545,1,0.6,0.384615,0.64,0,0,0.777778,0.56,0.0,0.0,0.0,0,0,0.090909]
number_of_instances
452 [245,44,15,15,13,25,3,2,9,50,0,0,0,4,5,22]
predictive_accuracy
0.6814159292035399
prior_entropy
2.412436334943334
relative_absolute_error
0.5390388300563774
root_mean_prior_squared_error
0.20549825255273738
root_mean_squared_error
0.17835393893190343
root_relative_squared_error
0.8679097594084512
total_cost
0
area_under_roc_curve
0.8384506918339443 [0.88381,0.817073,0.966667,0.965909,0.744318,0.727273,0.0,0.488889,0.0,0.897561,0.0,0.0,0.0,0.0,0.0,0.523256]
area_under_roc_curve
0.740928199584085 [0.760952,0.736585,1,0.732955,0.455556,1,0.0,0.488889,1,0.731707,0.0,0.0,0.0,0.0,0.488889,0.409091]
area_under_roc_curve
0.7436545689452667 [0.712,0.8275,1,0.453488,0.988636,0.982558,0.0,0.0,0.5,0.875,0.0,0.0,0.0,0.0,0.477273,0.656977]
area_under_roc_curve
0.8184002818886539 [0.872,0.73,0.988636,1,1,0.72093,0.0,0.0,0.5,0.845,0.0,0.0,0.0,0.0,0.431818,0.395349]
area_under_roc_curve
0.8794569919272132 [0.908,0.969512,1,0.732558,0.465909,0.97093,0.0,0.0,1,0.855,0.0,0.0,0.0,0.0,0.465909,0.69186]
area_under_roc_curve
0.7919864269467218 [0.834325,0.667683,1,0.988636,0.988636,0.825397,0.488636,0.0,1,0.77,0.0,0.0,0.0,0.0,0.431818,0.360465]
area_under_roc_curve
0.8012962976604499 [0.861111,0.783537,0.982558,0.988636,0.431818,0.821429,0.477273,0.0,1,0.63,0.0,0.0,0.0,0.443182,0.0,0.668605]
area_under_roc_curve
0.7690047832674038 [0.777778,0.667683,0.965116,1,0.443182,0.968254,0.477273,0.0,0.988636,0.765,0.0,0.0,0.0,0.454545,0.0,0.622093]
area_under_roc_curve
0.8538878139360274 [0.853175,0.823171,0.988372,1,0.418605,1,0.0,0.0,1,0.88,0.0,0.0,0.0,0.488636,0.0,0.976744]
area_under_roc_curve
0.8874356399257138 [0.925595,0.823171,1,1,0.965116,1,0.0,0.0,0.988636,0.88,0.0,0.0,0.0,0.465909,0.0,0.547619]
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
0.5050437121721587
kappa
0.41335333833458365
kappa
0.46892210857592437
kappa
0.5190839694656488
kappa
0.5468628969790861
kappa
0.5401459854014599
kappa
0.4992581602373886
kappa
0.5168711656441718
kappa
0.5614692653673163
kappa
0.5185185185185186
kb_relative_information_score
0.4619369084675441
kb_relative_information_score
0.4214682089800309
kb_relative_information_score
0.43233794064940817
kb_relative_information_score
0.5058359940190347
kb_relative_information_score
0.566507021574301
kb_relative_information_score
0.5035326204596287
kb_relative_information_score
0.4534408651203361
kb_relative_information_score
0.4556305656467952
kb_relative_information_score
0.5974512776634817
kb_relative_information_score
0.5945821138552971
mean_absolute_error
0.05140182884748103
mean_absolute_error
0.05122644565579348
mean_absolute_error
0.05210217251883919
mean_absolute_error
0.043727954144620815
mean_absolute_error
0.038585673585673594
mean_absolute_error
0.04424011174011174
mean_absolute_error
0.0502282671032671
mean_absolute_error
0.0523063973063973
mean_absolute_error
0.039957881624548296
mean_absolute_error
0.037091149591149584
mean_prior_absolute_error
0.08534234485321444
mean_prior_absolute_error
0.08546427907840952
mean_prior_absolute_error
0.08460351377018045
mean_prior_absolute_error
0.08460351377018045
mean_prior_absolute_error
0.08477564102564104
mean_prior_absolute_error
0.08615265906932575
mean_prior_absolute_error
0.08615859449192784
mean_prior_absolute_error
0.08615859449192784
mean_prior_absolute_error
0.08609924026590694
mean_prior_absolute_error
0.08605769230769232
number_of_instances
46 [25,5,1,2,2,2,0,1,0,5,0,0,0,0,0,3]
number_of_instances
46 [25,5,1,2,1,2,0,1,1,5,0,0,0,0,1,2]
number_of_instances
45 [25,5,1,2,1,2,0,0,1,5,0,0,0,0,1,2]
number_of_instances
45 [25,5,1,2,1,2,0,0,1,5,0,0,0,0,1,2]
number_of_instances
45 [25,4,2,2,1,2,0,0,1,5,0,0,0,0,1,2]
number_of_instances
45 [24,4,2,1,1,3,1,0,1,5,0,0,0,0,1,2]
number_of_instances
45 [24,4,2,1,1,3,1,0,1,5,0,0,0,1,0,2]
number_of_instances
45 [24,4,2,1,1,3,1,0,1,5,0,0,0,1,0,2]
number_of_instances
45 [24,4,2,1,2,3,0,0,1,5,0,0,0,1,0,2]
number_of_instances
45 [24,4,1,1,2,3,0,0,1,5,0,0,0,1,0,3]
predictive_accuracy
0.6521739130434783
predictive_accuracy
0.6304347826086957
predictive_accuracy
0.6666666666666667
predictive_accuracy
0.6888888888888889
predictive_accuracy
0.7111111111111111
predictive_accuracy
0.6888888888888889
predictive_accuracy
0.6666666666666667
predictive_accuracy
0.6888888888888889
predictive_accuracy
0.7111111111111111
predictive_accuracy
0.7111111111111111
prior_entropy
2.3800279266643507
prior_entropy
2.432724150197092
prior_entropy
2.324886859782311
prior_entropy
2.324886859782311
prior_entropy
2.3580391508118588
prior_entropy
2.4745297016384846
prior_entropy
2.480374910657013
prior_entropy
2.480374910657013
prior_entropy
2.440211467944622
prior_entropy
2.4285767578100224
relative_absolute_error
0.602301576502148
relative_absolute_error
0.5993901336112084
relative_absolute_error
0.6158393451644468
relative_absolute_error
0.5168574234801259
relative_absolute_error
0.4551504785909322
relative_absolute_error
0.5135083724405116
relative_absolute_error
0.582974541303282
relative_absolute_error
0.6070943660913348
relative_absolute_error
0.46409098966661355
relative_absolute_error
0.43100330251168223
root_mean_prior_squared_error
0.20501434405747065
root_mean_prior_squared_error
0.2053115084317244
root_mean_prior_squared_error
0.20320445414970847
root_mean_prior_squared_error
0.20320445414970847
root_mean_prior_squared_error
0.20362754588154708
root_mean_prior_squared_error
0.20698114765704212
root_mean_prior_squared_error
0.20699548523585812
root_mean_prior_squared_error
0.20699548523585812
root_mean_prior_squared_error
0.20685206472744588
root_mean_prior_squared_error
0.20675161117580884
root_mean_squared_error
0.18357069350608918
root_mean_squared_error
0.19615446344359497
root_mean_squared_error
0.189177862885402
root_mean_squared_error
0.18053555086186351
root_mean_squared_error
0.1631649956243515
root_mean_squared_error
0.1774999014836193
root_mean_squared_error
0.18039279816216747
root_mean_squared_error
0.19479432806658817
root_mean_squared_error
0.16199593197048717
root_mean_squared_error
0.14998982724583784
root_relative_squared_error
0.8954041452564399
root_relative_squared_error
0.9553992610639526
root_relative_squared_error
0.9309730127570306
root_relative_squared_error
0.8884428819107285
root_relative_squared_error
0.8012913720389616
root_relative_squared_error
0.8575655488089581
root_relative_squared_error
0.87148180046836
root_relative_squared_error
0.9410559261455993
root_relative_squared_error
0.7831487308765207
root_relative_squared_error
0.7254590491113306
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
62.5
usercpu_time_millis
62.5
usercpu_time_millis
62.5
usercpu_time_millis
62.5
usercpu_time_millis
62.5
usercpu_time_millis
62.5
usercpu_time_millis
78.125
usercpu_time_millis
62.5
usercpu_time_millis
62.5
usercpu_time_millis
62.5
usercpu_time_millis_testing
15.625
usercpu_time_millis_testing
0
usercpu_time_millis_testing
15.625
usercpu_time_millis_testing
0
usercpu_time_millis_testing
15.625
usercpu_time_millis_testing
15.625
usercpu_time_millis_testing
0
usercpu_time_millis_testing
0
usercpu_time_millis_testing
0
usercpu_time_millis_testing
0
usercpu_time_millis_training
46.875
usercpu_time_millis_training
62.5
usercpu_time_millis_training
46.875
usercpu_time_millis_training
62.5
usercpu_time_millis_training
46.875
usercpu_time_millis_training
46.875
usercpu_time_millis_training
78.125
usercpu_time_millis_training
62.5
usercpu_time_millis_training
62.5
usercpu_time_millis_training
62.5
wall_clock_time_millis
62.50715255737305
wall_clock_time_millis
62.50596046447754
wall_clock_time_millis
62.50643730163574
wall_clock_time_millis
78.13310623168945
wall_clock_time_millis
62.50929832458496
wall_clock_time_millis
78.13334465026855
wall_clock_time_millis
78.12809944152832
wall_clock_time_millis
62.50810623168945
wall_clock_time_millis
62.53385543823242
wall_clock_time_millis
62.506675720214844
wall_clock_time_millis_testing
15.626668930053711
wall_clock_time_millis_testing
0
wall_clock_time_millis_testing
15.62809944152832
wall_clock_time_millis_testing
15.625953674316406
wall_clock_time_millis_testing
15.62952995300293
wall_clock_time_millis_testing
15.625953674316406
wall_clock_time_millis_testing
0
wall_clock_time_millis_testing
0
wall_clock_time_millis_testing
0
wall_clock_time_millis_testing
0
wall_clock_time_millis_training
46.880483627319336
wall_clock_time_millis_training
62.50596046447754
wall_clock_time_millis_training
46.87833786010742
wall_clock_time_millis_training
62.50715255737305
wall_clock_time_millis_training
46.87976837158203
wall_clock_time_millis_training
62.50739097595215
wall_clock_time_millis_training
78.12809944152832
wall_clock_time_millis_training
62.50810623168945
wall_clock_time_millis_training
62.53385543823242
wall_clock_time_millis_training
62.506675720214844
weighted_recall
0.6521739130434783 [0.76,0.6,1,1,0.5,0.5,0.0,0,0.0,0.6,0.0,0.0,0.0,0.0,0.0,0]
weighted_recall
0.6304347826086957 [0.8,0.4,1,0.5,0,0.5,0.0,0,1,0.6,0.0,0.0,0.0,0.0,0,0]
weighted_recall
0.6666666666666666 [0.8,0.4,1,0,1,0.5,0.0,0.0,0,0.8,0.0,0.0,0.0,0.0,0,0.5]
weighted_recall
0.6888888888888889 [0.84,0.2,1,1,1,0.5,0.0,0.0,0,0.8,0.0,0.0,0.0,0.0,0,0]
weighted_recall
0.7111111111111111 [0.88,0.75,1,0.5,0,0.5,0.0,0.0,1,0.4,0.0,0.0,0.0,0.0,0,0]
weighted_recall
0.6888888888888889 [0.833333,0.25,1,1,1,0.666667,0,0.0,1,0.6,0.0,0.0,0.0,0.0,0,0]
weighted_recall
0.6666666666666666 [0.833333,0.5,1,1,0,0.666667,0,0.0,1,0.2,0.0,0.0,0.0,0,0.0,0.5]
weighted_recall
0.6888888888888889 [0.875,0.5,1,1,0,0.666667,0,0.0,1,0.4,0.0,0.0,0.0,0,0.0,0]
weighted_recall
0.7111111111111111 [0.833333,0.5,1,0,0,1,0.0,0.0,1,0.8,0.0,0.0,0.0,0,0.0,0]
weighted_recall
0.7111111111111111 [0.958333,0.5,1,0,0.5,0.666667,0.0,0.0,1,0.4,0.0,0.0,0.0,0,0.0,0]