Run
10228462

Run 10228462

Task 40 (Supervised Classification) glass Uploaded 08-06-2019 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.C376be63e1e0e09(n3=sklearn.pipeline.C376be63e1e08ac(n4=skl earn.pipeline.C376be63e1dfbb3(n5=sklearn.compose._column_transformer.C376be 63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae)), n7=sklearn.pipeline.C376be63e1e05aa(n8=sklearn.compose._column_transformer. C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0 300))),n10=fastsklearnfeature.transformations.IdentityTransformation.C376be 63e1e0ca8,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_state30010
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.C376be63e1e0e09(n3=sklearn.pipeline.C376be63e1e08ac(n4=sklearn.pipeline.C376be63e1dfbb3(n5=sklearn.compose._column_transformer.C376be63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae)),n7=sklearn.pipeline.C376be63e1e05aa(n8=sklearn.compose._column_transformer.C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0300))),n10=fastsklearnfeature.transformations.IdentityTransformation.C376be63e1e0ca8,c=sklearn.linear_model.logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.C376be63e1e0e09(n3=sklearn.pipeline.C376be63e1e08ac(n4=sklearn.pipeline.C376be63e1dfbb3(n5=sklearn.compose._column_transformer.C376be63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae)),n7=sklearn.pipeline.C376be63e1e05aa(n8=sklearn.compose._column_transformer.C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0300))),n10=fastsklearnfeature.transformations.IdentityTransformation.C376be63e1e0ca8,c=sklearn.linear_model.logistic.LogisticRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n3", "step_name": "n3"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n10", "step_name": "n10"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "c", "step_name": "c"}}]
sklearn.pipeline.C376be63e1e08ac(n4=sklearn.pipeline.C376be63e1dfbb3(n5=sklearn.compose._column_transformer.C376be63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae)),n7=sklearn.pipeline.C376be63e1e05aa(n8=sklearn.compose._column_transformer.C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0300)))(1)_n_jobsnull
sklearn.pipeline.C376be63e1e08ac(n4=sklearn.pipeline.C376be63e1dfbb3(n5=sklearn.compose._column_transformer.C376be63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae)),n7=sklearn.pipeline.C376be63e1e05aa(n8=sklearn.compose._column_transformer.C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0300)))(1)_transformer_list[{"oml-python:serialized_object": "component_reference", "value": {"key": "n4", "step_name": "n4"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n7", "step_name": "n7"}}]
sklearn.pipeline.C376be63e1e08ac(n4=sklearn.pipeline.C376be63e1dfbb3(n5=sklearn.compose._column_transformer.C376be63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae)),n7=sklearn.pipeline.C376be63e1e05aa(n8=sklearn.compose._column_transformer.C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0300)))(1)_transformer_weightsnull
sklearn.pipeline.C376be63e1dfbb3(n5=sklearn.compose._column_transformer.C376be63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae))(1)_memorynull
sklearn.pipeline.C376be63e1dfbb3(n5=sklearn.compose._column_transformer.C376be63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae))(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n5", "step_name": "n5"}}]
sklearn.compose._column_transformer.C376be63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae)(1)_n_jobsnull
sklearn.compose._column_transformer.C376be63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae)(1)_remainder"drop"
sklearn.compose._column_transformer.C376be63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C376be63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C376be63e1df8f6(n6=sklearn.preprocessing._function_transformer.C376be63e1df6ae)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n6", "step_name": "n6", "argument_1": [3]}}]
sklearn.preprocessing._function_transformer.C376be63e1df6ae(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C376be63e1df6ae(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C376be63e1df6ae(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C376be63e1df6ae(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C376be63e1df6ae(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C376be63e1df6ae(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C376be63e1df6ae(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C376be63e1df6ae(1)_validatefalse
sklearn.pipeline.C376be63e1e05aa(n8=sklearn.compose._column_transformer.C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0300))(1)_memorynull
sklearn.pipeline.C376be63e1e05aa(n8=sklearn.compose._column_transformer.C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0300))(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n8", "step_name": "n8"}}]
sklearn.compose._column_transformer.C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0300)(1)_n_jobsnull
sklearn.compose._column_transformer.C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0300)(1)_remainder"drop"
sklearn.compose._column_transformer.C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0300)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0300)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C376be63e1e0428(n9=sklearn.preprocessing._function_transformer.C376be63e1e0300)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n9", "step_name": "n9", "argument_1": [5]}}]
sklearn.preprocessing._function_transformer.C376be63e1e0300(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C376be63e1e0300(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C376be63e1e0300(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C376be63e1e0300(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C376be63e1e0300(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C376be63e1e0300(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C376be63e1e0300(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C376be63e1e0300(1)_validatefalse
fastsklearnfeature.transformations.IdentityTransformation.C376be63e1e0ca8(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
0.7132 ± 0.0629
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5262 ± 0.13
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3915 ± 0.1014
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1152 ± 0.0303
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2245 ± 0.0029
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2116 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
Number of instances
214
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5622 ± 0.1559
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5374 ± 0.0765
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.1835 ± 0.0468
Cross-validation details (10-fold Crossvalidation)
Recall
0.5374 ± 0.0765
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0611 ± 0.0161
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3244 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3296 ± 0.003
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.016 ± 0.0116
Cross-validation details (10-fold Crossvalidation)