Run
10231164

Run 10231164

Task 10093 (Supervised Classification) banknote-authentication Uploaded 18-07-2019 by Felix Neutatz
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • ComplexityDriven openml-python Sklearn_0.20.3.
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.C378b7401584453(n307=sklearn.pipeline.C378b7401584060(n308 =sklearn.pipeline.C58df200226bb8(n309=sklearn.compose._column_transformer.C 378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b740158 30f1)),n311=sklearn.pipeline.C378b7401583dd0(n312=sklearn.compose._column_t ransformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer .C378b7401583b6b))),n314=fastsklearnfeature.transformations.IdentityTransfo rmation.C378b740158434c,c=sklearn.linear_model.logistic.LogisticRegression) (1)Automatically created scikit-learn flow.
sklearn.linear_model.logistic.LogisticRegression(23)_C0.1
sklearn.linear_model.logistic.LogisticRegression(23)_class_weight"balanced"
sklearn.linear_model.logistic.LogisticRegression(23)_dualfalse
sklearn.linear_model.logistic.LogisticRegression(23)_fit_intercepttrue
sklearn.linear_model.logistic.LogisticRegression(23)_intercept_scaling1
sklearn.linear_model.logistic.LogisticRegression(23)_max_iter10000
sklearn.linear_model.logistic.LogisticRegression(23)_multi_class"auto"
sklearn.linear_model.logistic.LogisticRegression(23)_n_jobsnull
sklearn.linear_model.logistic.LogisticRegression(23)_penalty"l2"
sklearn.linear_model.logistic.LogisticRegression(23)_random_state55825
sklearn.linear_model.logistic.LogisticRegression(23)_solver"lbfgs"
sklearn.linear_model.logistic.LogisticRegression(23)_tol0.0001
sklearn.linear_model.logistic.LogisticRegression(23)_verbose0
sklearn.linear_model.logistic.LogisticRegression(23)_warm_startfalse
sklearn.pipeline.C378b7401584453(n307=sklearn.pipeline.C378b7401584060(n308=sklearn.pipeline.C58df200226bb8(n309=sklearn.compose._column_transformer.C378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b74015830f1)),n311=sklearn.pipeline.C378b7401583dd0(n312=sklearn.compose._column_transformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer.C378b7401583b6b))),n314=fastsklearnfeature.transformations.IdentityTransformation.C378b740158434c,c=sklearn.linear_model.logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.C378b7401584453(n307=sklearn.pipeline.C378b7401584060(n308=sklearn.pipeline.C58df200226bb8(n309=sklearn.compose._column_transformer.C378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b74015830f1)),n311=sklearn.pipeline.C378b7401583dd0(n312=sklearn.compose._column_transformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer.C378b7401583b6b))),n314=fastsklearnfeature.transformations.IdentityTransformation.C378b740158434c,c=sklearn.linear_model.logistic.LogisticRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n307", "step_name": "n307"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n314", "step_name": "n314"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "c", "step_name": "c"}}]
sklearn.pipeline.C378b7401584060(n308=sklearn.pipeline.C58df200226bb8(n309=sklearn.compose._column_transformer.C378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b74015830f1)),n311=sklearn.pipeline.C378b7401583dd0(n312=sklearn.compose._column_transformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer.C378b7401583b6b)))(1)_n_jobsnull
sklearn.pipeline.C378b7401584060(n308=sklearn.pipeline.C58df200226bb8(n309=sklearn.compose._column_transformer.C378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b74015830f1)),n311=sklearn.pipeline.C378b7401583dd0(n312=sklearn.compose._column_transformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer.C378b7401583b6b)))(1)_transformer_list[{"oml-python:serialized_object": "component_reference", "value": {"key": "n308", "step_name": "n308"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n311", "step_name": "n311"}}]
sklearn.pipeline.C378b7401584060(n308=sklearn.pipeline.C58df200226bb8(n309=sklearn.compose._column_transformer.C378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b74015830f1)),n311=sklearn.pipeline.C378b7401583dd0(n312=sklearn.compose._column_transformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer.C378b7401583b6b)))(1)_transformer_weightsnull
sklearn.pipeline.C58df200226bb8(n309=sklearn.compose._column_transformer.C378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b74015830f1))(1)_memorynull
sklearn.pipeline.C58df200226bb8(n309=sklearn.compose._column_transformer.C378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b74015830f1))(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n309", "step_name": "n309"}}]
sklearn.compose._column_transformer.C378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b74015830f1)(1)_n_jobsnull
sklearn.compose._column_transformer.C378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b74015830f1)(1)_remainder"drop"
sklearn.compose._column_transformer.C378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b74015830f1)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b74015830f1)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C378b7401583322(n310=sklearn.preprocessing._function_transformer.C378b74015830f1)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n310", "step_name": "n310", "argument_1": [0]}}]
sklearn.preprocessing._function_transformer.C378b74015830f1(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C378b74015830f1(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C378b74015830f1(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C378b74015830f1(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C378b74015830f1(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C378b74015830f1(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C378b74015830f1(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C378b74015830f1(1)_validatefalse
sklearn.pipeline.C378b7401583dd0(n312=sklearn.compose._column_transformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer.C378b7401583b6b))(1)_memorynull
sklearn.pipeline.C378b7401583dd0(n312=sklearn.compose._column_transformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer.C378b7401583b6b))(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n312", "step_name": "n312"}}]
sklearn.compose._column_transformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer.C378b7401583b6b)(1)_n_jobsnull
sklearn.compose._column_transformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer.C378b7401583b6b)(1)_remainder"drop"
sklearn.compose._column_transformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer.C378b7401583b6b)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer.C378b7401583b6b)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C378b7401583c80(n313=sklearn.preprocessing._function_transformer.C378b7401583b6b)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n313", "step_name": "n313", "argument_1": [2]}}]
sklearn.preprocessing._function_transformer.C378b7401583b6b(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C378b7401583b6b(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C378b7401583b6b(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C378b7401583b6b(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C378b7401583b6b(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C378b7401583b6b(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C378b7401583b6b(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C378b7401583b6b(1)_validatefalse
fastsklearnfeature.transformations.IdentityTransformation.C378b740158434c(1)_number_parent_featuresnull

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

17 Evaluation measures

Area under ROC curve
0.9347 ± 0.0265
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8808 ± 0.0329
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7602 ± 0.066
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6354 ± 0.0553
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1923 ± 0.0261
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4939 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Number of instances
1372
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8843 ± 0.0325
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8805 ± 0.033
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9911 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Recall
0.8805 ± 0.033
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3894 ± 0.0529
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4969 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3076 ± 0.035
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6191 ± 0.0705
Cross-validation details (10-fold Crossvalidation)