Run
10228794

Run 10228794

Task 3656 (Supervised Classification) diabetes_numeric Uploaded 21-06-2019 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.C37764fd1df4b51(n29=sklearn.pipeline.C37764fd1df45d7(n30=s klearn.pipeline.C37764fd1df35d2(n31=sklearn.compose._column_transformer.C37 764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8 d),n33=fastsklearnfeature.transformations.OneDivisionTransformation.Ce32c48 fd6d6),n34=sklearn.pipeline.C58bd4c82fecfd(n35=sklearn.compose._column_tran sformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C377 64fd1df3cf0),n37=fastsklearnfeature.transformations.MinMaxScalingTransforma tion.C37764fd1df400a)),n38=fastsklearnfeature.transformations.IdentityTrans formation.C37764fd1df49fa,c=sklearn.linear_model.logistic.LogisticRegressio n)(1)Automatically created scikit-learn flow.
sklearn.linear_model.logistic.LogisticRegression(23)_C0.001
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_state44073
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.C37764fd1df4b51(n29=sklearn.pipeline.C37764fd1df45d7(n30=sklearn.pipeline.C37764fd1df35d2(n31=sklearn.compose._column_transformer.C37764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8d),n33=fastsklearnfeature.transformations.OneDivisionTransformation.Ce32c48fd6d6),n34=sklearn.pipeline.C58bd4c82fecfd(n35=sklearn.compose._column_transformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C37764fd1df3cf0),n37=fastsklearnfeature.transformations.MinMaxScalingTransformation.C37764fd1df400a)),n38=fastsklearnfeature.transformations.IdentityTransformation.C37764fd1df49fa,c=sklearn.linear_model.logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.C37764fd1df4b51(n29=sklearn.pipeline.C37764fd1df45d7(n30=sklearn.pipeline.C37764fd1df35d2(n31=sklearn.compose._column_transformer.C37764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8d),n33=fastsklearnfeature.transformations.OneDivisionTransformation.Ce32c48fd6d6),n34=sklearn.pipeline.C58bd4c82fecfd(n35=sklearn.compose._column_transformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C37764fd1df3cf0),n37=fastsklearnfeature.transformations.MinMaxScalingTransformation.C37764fd1df400a)),n38=fastsklearnfeature.transformations.IdentityTransformation.C37764fd1df49fa,c=sklearn.linear_model.logistic.LogisticRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n29", "step_name": "n29"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n38", "step_name": "n38"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "c", "step_name": "c"}}]
sklearn.pipeline.C37764fd1df45d7(n30=sklearn.pipeline.C37764fd1df35d2(n31=sklearn.compose._column_transformer.C37764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8d),n33=fastsklearnfeature.transformations.OneDivisionTransformation.Ce32c48fd6d6),n34=sklearn.pipeline.C58bd4c82fecfd(n35=sklearn.compose._column_transformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C37764fd1df3cf0),n37=fastsklearnfeature.transformations.MinMaxScalingTransformation.C37764fd1df400a))(1)_n_jobsnull
sklearn.pipeline.C37764fd1df45d7(n30=sklearn.pipeline.C37764fd1df35d2(n31=sklearn.compose._column_transformer.C37764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8d),n33=fastsklearnfeature.transformations.OneDivisionTransformation.Ce32c48fd6d6),n34=sklearn.pipeline.C58bd4c82fecfd(n35=sklearn.compose._column_transformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C37764fd1df3cf0),n37=fastsklearnfeature.transformations.MinMaxScalingTransformation.C37764fd1df400a))(1)_transformer_list[{"oml-python:serialized_object": "component_reference", "value": {"key": "n30", "step_name": "n30"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n34", "step_name": "n34"}}]
sklearn.pipeline.C37764fd1df45d7(n30=sklearn.pipeline.C37764fd1df35d2(n31=sklearn.compose._column_transformer.C37764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8d),n33=fastsklearnfeature.transformations.OneDivisionTransformation.Ce32c48fd6d6),n34=sklearn.pipeline.C58bd4c82fecfd(n35=sklearn.compose._column_transformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C37764fd1df3cf0),n37=fastsklearnfeature.transformations.MinMaxScalingTransformation.C37764fd1df400a))(1)_transformer_weightsnull
sklearn.pipeline.C37764fd1df35d2(n31=sklearn.compose._column_transformer.C37764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8d),n33=fastsklearnfeature.transformations.OneDivisionTransformation.Ce32c48fd6d6)(1)_memorynull
sklearn.pipeline.C37764fd1df35d2(n31=sklearn.compose._column_transformer.C37764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8d),n33=fastsklearnfeature.transformations.OneDivisionTransformation.Ce32c48fd6d6)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n31", "step_name": "n31"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n33", "step_name": "n33"}}]
sklearn.compose._column_transformer.C37764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8d)(1)_n_jobsnull
sklearn.compose._column_transformer.C37764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8d)(1)_remainder"drop"
sklearn.compose._column_transformer.C37764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8d)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C37764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8d)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C37764fd1df3029(n32=sklearn.preprocessing._function_transformer.C37764fd1df2d8d)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n32", "step_name": "n32", "argument_1": [0]}}]
sklearn.preprocessing._function_transformer.C37764fd1df2d8d(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C37764fd1df2d8d(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C37764fd1df2d8d(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C37764fd1df2d8d(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C37764fd1df2d8d(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C37764fd1df2d8d(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C37764fd1df2d8d(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C37764fd1df2d8d(1)_validatefalse
sklearn.pipeline.C58bd4c82fecfd(n35=sklearn.compose._column_transformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C37764fd1df3cf0),n37=fastsklearnfeature.transformations.MinMaxScalingTransformation.C37764fd1df400a)(1)_memorynull
sklearn.pipeline.C58bd4c82fecfd(n35=sklearn.compose._column_transformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C37764fd1df3cf0),n37=fastsklearnfeature.transformations.MinMaxScalingTransformation.C37764fd1df400a)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "n35", "step_name": "n35"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "n37", "step_name": "n37"}}]
sklearn.compose._column_transformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C37764fd1df3cf0)(1)_n_jobsnull
sklearn.compose._column_transformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C37764fd1df3cf0)(1)_remainder"drop"
sklearn.compose._column_transformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C37764fd1df3cf0)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C37764fd1df3cf0)(1)_transformer_weightsnull
sklearn.compose._column_transformer.C58bd4c82fec9e(n36=sklearn.preprocessing._function_transformer.C37764fd1df3cf0)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "n36", "step_name": "n36", "argument_1": [0]}}]
sklearn.preprocessing._function_transformer.C37764fd1df3cf0(1)_accept_sparsefalse
sklearn.preprocessing._function_transformer.C37764fd1df3cf0(1)_check_inversetrue
sklearn.preprocessing._function_transformer.C37764fd1df3cf0(1)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.C37764fd1df3cf0(1)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.C37764fd1df3cf0(1)_inverse_funcnull
sklearn.preprocessing._function_transformer.C37764fd1df3cf0(1)_kw_argsnull
sklearn.preprocessing._function_transformer.C37764fd1df3cf0(1)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.C37764fd1df3cf0(1)_validatefalse
fastsklearnfeature.transformations.IdentityTransformation.C37764fd1df49fa(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
0.5928 ± 0.403
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6518 ± 0.1174
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2879 ± 0.2704
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.0693 ± 0.0797
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4999 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4791 ± 0.0218
Cross-validation details (10-fold Crossvalidation)
Number of instances
43
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.738 ± 0.153
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6977 ± 0.1165
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9682 ± 0.0639
Cross-validation details (10-fold Crossvalidation)
Recall
0.6977 ± 0.1165
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0435 ± 0.0485
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4889 ± 0.0226
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4999 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0224 ± 0.0483
Cross-validation details (10-fold Crossvalidation)