Run
10228499

Run 10228499

Task 10101 (Supervised Classification) blood-transfusion-service-center Uploaded 16-06-2019 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.C58b630cbf2cf3(n20=sklearn.pipeline.C3771de7f77b146(n21=sk learn.pipeline.C58b630cbf2798(n22=sklearn.compose._column_transformer.C3771 de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71) ,n24=fastsklearnfeature.transformations.mdlp_discretization.MDLPDiscretizer Transformation.C3771de7f7786ad),n25=sklearn.pipeline.C3771de7f77a72b(n26=sk learn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing. _function_transformer.C3771de7f7799a1),n28=fastsklearnfeature.transformatio ns.OneDivisionTransformation.C3771de7f77a33b)),n29=fastsklearnfeature.trans formations.IdentityTransformation.C3771de7f77bdd4,c=sklearn.linear_model.lo gistic.LogisticRegression)(1)Automatically created scikit-learn flow.
sklearn.linear_model.logistic.LogisticRegression(23)_C1
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_state29520
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.C58b630cbf2cf3(n20=sklearn.pipeline.C3771de7f77b146(n21=sklearn.pipeline.C58b630cbf2798(n22=sklearn.compose._column_transformer.C3771de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71),n24=fastsklearnfeature.transformations.mdlp_discretization.MDLPDiscretizerTransformation.C3771de7f7786ad),n25=sklearn.pipeline.C3771de7f77a72b(n26=sklearn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing._function_transformer.C3771de7f7799a1),n28=fastsklearnfeature.transformations.OneDivisionTransformation.C3771de7f77a33b)),n29=fastsklearnfeature.transformations.IdentityTransformation.C3771de7f77bdd4,c=sklearn.linear_model.logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.C58b630cbf2cf3(n20=sklearn.pipeline.C3771de7f77b146(n21=sklearn.pipeline.C58b630cbf2798(n22=sklearn.compose._column_transformer.C3771de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71),n24=fastsklearnfeature.transformations.mdlp_discretization.MDLPDiscretizerTransformation.C3771de7f7786ad),n25=sklearn.pipeline.C3771de7f77a72b(n26=sklearn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing._function_transformer.C3771de7f7799a1),n28=fastsklearnfeature.transformations.OneDivisionTransformation.C3771de7f77a33b)),n29=fastsklearnfeature.transformations.IdentityTransformation.C3771de7f77bdd4,c=sklearn.linear_model.logistic.LogisticRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n20", "step_name": "n20"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n29", "step_name": "n29"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "c", "step_name": "c"}}]
sklearn.pipeline.C3771de7f77b146(n21=sklearn.pipeline.C58b630cbf2798(n22=sklearn.compose._column_transformer.C3771de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71),n24=fastsklearnfeature.transformations.mdlp_discretization.MDLPDiscretizerTransformation.C3771de7f7786ad),n25=sklearn.pipeline.C3771de7f77a72b(n26=sklearn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing._function_transformer.C3771de7f7799a1),n28=fastsklearnfeature.transformations.OneDivisionTransformation.C3771de7f77a33b))(1)_n_jobsnull
sklearn.pipeline.C3771de7f77b146(n21=sklearn.pipeline.C58b630cbf2798(n22=sklearn.compose._column_transformer.C3771de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71),n24=fastsklearnfeature.transformations.mdlp_discretization.MDLPDiscretizerTransformation.C3771de7f7786ad),n25=sklearn.pipeline.C3771de7f77a72b(n26=sklearn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing._function_transformer.C3771de7f7799a1),n28=fastsklearnfeature.transformations.OneDivisionTransformation.C3771de7f77a33b))(1)_transformer_list[{"oml-python:serialized_object": "component_reference", "value": {"key": "n21", "step_name": "n21"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n25", "step_name": "n25"}}]
sklearn.pipeline.C3771de7f77b146(n21=sklearn.pipeline.C58b630cbf2798(n22=sklearn.compose._column_transformer.C3771de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71),n24=fastsklearnfeature.transformations.mdlp_discretization.MDLPDiscretizerTransformation.C3771de7f7786ad),n25=sklearn.pipeline.C3771de7f77a72b(n26=sklearn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing._function_transformer.C3771de7f7799a1),n28=fastsklearnfeature.transformations.OneDivisionTransformation.C3771de7f77a33b))(1)_transformer_weightsnull
sklearn.pipeline.C58b630cbf2798(n22=sklearn.compose._column_transformer.C3771de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71),n24=fastsklearnfeature.transformations.mdlp_discretization.MDLPDiscretizerTransformation.C3771de7f7786ad)(1)_memorynull
sklearn.pipeline.C58b630cbf2798(n22=sklearn.compose._column_transformer.C3771de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71),n24=fastsklearnfeature.transformations.mdlp_discretization.MDLPDiscretizerTransformation.C3771de7f7786ad)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n22", "step_name": "n22"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n24", "step_name": "n24"}}]
sklearn.compose._column_transformer.C3771de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71)(1)_n_jobsnull
sklearn.compose._column_transformer.C3771de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71)(1)_remainder"drop"
sklearn.compose._column_transformer.C3771de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C3771de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C3771de7f7781df(n23=sklearn.preprocessing._function_transformer.C3771de7f777e71)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n23", "step_name": "n23", "argument_1": [0]}}]
sklearn.preprocessing._function_transformer.C3771de7f777e71(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C3771de7f777e71(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C3771de7f777e71(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C3771de7f777e71(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C3771de7f777e71(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C3771de7f777e71(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C3771de7f777e71(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C3771de7f777e71(1)_validatefalse
sklearn.pipeline.C3771de7f77a72b(n26=sklearn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing._function_transformer.C3771de7f7799a1),n28=fastsklearnfeature.transformations.OneDivisionTransformation.C3771de7f77a33b)(1)_memorynull
sklearn.pipeline.C3771de7f77a72b(n26=sklearn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing._function_transformer.C3771de7f7799a1),n28=fastsklearnfeature.transformations.OneDivisionTransformation.C3771de7f77a33b)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n26", "step_name": "n26"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n28", "step_name": "n28"}}]
sklearn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing._function_transformer.C3771de7f7799a1)(1)_n_jobsnull
sklearn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing._function_transformer.C3771de7f7799a1)(1)_remainder"drop"
sklearn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing._function_transformer.C3771de7f7799a1)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing._function_transformer.C3771de7f7799a1)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C58b630cbf2948(n27=sklearn.preprocessing._function_transformer.C3771de7f7799a1)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n27", "step_name": "n27", "argument_1": [1]}}]
sklearn.preprocessing._function_transformer.C3771de7f7799a1(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C3771de7f7799a1(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C3771de7f7799a1(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C3771de7f7799a1(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C3771de7f7799a1(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C3771de7f7799a1(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C3771de7f7799a1(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C3771de7f7799a1(1)_validatefalse
fastsklearnfeature.transformations.IdentityTransformation.C3771de7f77bdd4(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

0.7353 ± 0.0457
Per class
Cross-validation details (10-fold Crossvalidation)
0.7107 ± 0.0403
Per class
Cross-validation details (10-fold Crossvalidation)
0.3057 ± 0.0894
Cross-validation details (10-fold Crossvalidation)
-0.317 ± 0.0658
Cross-validation details (10-fold Crossvalidation)
0.4174 ± 0.0161
Cross-validation details (10-fold Crossvalidation)
0.363 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
748
Per class
Cross-validation details (10-fold Crossvalidation)
0.7646 ± 0.0375
Per class
Cross-validation details (10-fold Crossvalidation)
0.6898 ± 0.0444
Cross-validation details (10-fold Crossvalidation)
0.7916 ± 0.0072
Cross-validation details (10-fold Crossvalidation)
0.6898 ± 0.0444
Per class
Cross-validation details (10-fold Crossvalidation)
1.1497 ± 0.0448
Cross-validation details (10-fold Crossvalidation)
0.4258 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
0.4578 ± 0.0164
Cross-validation details (10-fold Crossvalidation)
1.0751 ± 0.0396
Cross-validation details (10-fold Crossvalidation)