Run
10228047

Run 10228047

Task 3876 (Supervised Classification) analcatdata_challenger Uploaded 13-05-2019 by Felix Neutatz
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Flow

fastsklearnfeature.feature_selection.openml_wrapper.ComplexPipelineWrapper. ComplexPipelineWrapper(my_pipeline=sklearn.pipeline.Pipeline(features=sklea rn.pipeline.Pipeline(parents=sklearn.pipeline.FeatureUnion(Temperature15577 73213.1252477=sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column _transformer.ColumnTransformer(identity=sklearn.preprocessing._function_tra nsformer.FunctionTransformer))),MinMaxScaling=fastsklearnfeature.transforma tions.MinMaxScalingTransformation.MinMaxScalingTransformation),classifier=s klearn.linear_model.logistic.LogisticRegression))(1)Automatically created scikit-learn flow.
sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer)(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "identity", "step_name": "identity", "argument_1": [0]}}]
sklearn.preprocessing._function_transformer.FunctionTransformer(4)_accept_sparsefalse
sklearn.preprocessing._function_transformer.FunctionTransformer(4)_check_inversetrue
sklearn.preprocessing._function_transformer.FunctionTransformer(4)_func{"oml-python:serialized_object": "function", "value": "fastsklearnfeature.candidates.Identity.identity"}
sklearn.preprocessing._function_transformer.FunctionTransformer(4)_inv_kw_argsnull
sklearn.preprocessing._function_transformer.FunctionTransformer(4)_inverse_funcnull
sklearn.preprocessing._function_transformer.FunctionTransformer(4)_kw_argsnull
sklearn.preprocessing._function_transformer.FunctionTransformer(4)_pass_y"deprecated"
sklearn.preprocessing._function_transformer.FunctionTransformer(4)_validatefalse
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_state46668
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.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer))(1)_memorynull
sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer))(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Temperature", "step_name": "Temperature"}}]
sklearn.pipeline.Pipeline(features=sklearn.pipeline.Pipeline(parents=sklearn.pipeline.FeatureUnion(Temperature1557773213.1252477=sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer))),MinMaxScaling=fastsklearnfeature.transformations.MinMaxScalingTransformation.MinMaxScalingTransformation),classifier=sklearn.linear_model.logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.Pipeline(features=sklearn.pipeline.Pipeline(parents=sklearn.pipeline.FeatureUnion(Temperature1557773213.1252477=sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer))),MinMaxScaling=fastsklearnfeature.transformations.MinMaxScalingTransformation.MinMaxScalingTransformation),classifier=sklearn.linear_model.logistic.LogisticRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "features", "step_name": "features"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
sklearn.pipeline.Pipeline(parents=sklearn.pipeline.FeatureUnion(Temperature1557773213.1252477=sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer))),MinMaxScaling=fastsklearnfeature.transformations.MinMaxScalingTransformation.MinMaxScalingTransformation)(1)_memorynull
sklearn.pipeline.Pipeline(parents=sklearn.pipeline.FeatureUnion(Temperature1557773213.1252477=sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer))),MinMaxScaling=fastsklearnfeature.transformations.MinMaxScalingTransformation.MinMaxScalingTransformation)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "parents", "step_name": "parents"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "MinMaxScaling", "step_name": "MinMaxScaling"}}]
sklearn.pipeline.FeatureUnion(Temperature1557773213.1252477=sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer)))(1)_n_jobsnull
sklearn.pipeline.FeatureUnion(Temperature1557773213.1252477=sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer)))(1)_transformer_list[{"oml-python:serialized_object": "component_reference", "value": {"key": "Temperature1557773213.1252477", "step_name": "Temperature1557773213.1252477"}}]
sklearn.pipeline.FeatureUnion(Temperature1557773213.1252477=sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer)))(1)_transformer_weightsnull

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
0.6576 ± 0.2308
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8076 ± 0.0858
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1316 ± 0.3388
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-239.5837 ± 14.3285
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2536 ± 0.1161
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1273 ± 0.0196
Cross-validation details (10-fold Crossvalidation)
Number of instances
138
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9072 ± 0.057
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7464 ± 0.1161
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3712
Cross-validation details (10-fold Crossvalidation)
Recall
0.7464 ± 0.1161
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.9919 ± 0.8633
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.247 ± 0.0592
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5036 ± 0.1817
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
2.039 ± 0.6985
Cross-validation details (10-fold Crossvalidation)