Run
10228043

Run 10228043

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(pipeline=sklearn.pipeline.Pipeline(features=sklearn. pipeline.FeatureUnion(Temperature=sklearn.pipeline.Pipeline(Temperature=skl earn.compose._column_transformer.ColumnTransformer(identity=sklearn.preproc essing._function_transformer.FunctionTransformer))),classifier=sklearn.line ar_model.logistic.LogisticRegression))(2)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_state14841
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(features=sklearn.pipeline.FeatureUnion(Temperature=sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer))),classifier=sklearn.linear_model.logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.Pipeline(features=sklearn.pipeline.FeatureUnion(Temperature=sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer))),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.FeatureUnion(Temperature=sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer)))(1)_n_jobsnull
sklearn.pipeline.FeatureUnion(Temperature=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": "Temperature", "step_name": "Temperature"}}]
sklearn.pipeline.FeatureUnion(Temperature=sklearn.pipeline.Pipeline(Temperature=sklearn.compose._column_transformer.ColumnTransformer(identity=sklearn.preprocessing._function_transformer.FunctionTransformer)))(1)_transformer_weightsnull
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"}}]

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
0.6499 ± 0.2221
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7976 ± 0.0876
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.12 ± 0.3349
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-260.6716 ± 14.8367
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2681 ± 0.1196
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
0.906 ± 0.0567
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7319 ± 0.1196
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3712
Cross-validation details (10-fold Crossvalidation)
Recall
0.7319 ± 0.1196
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
2.1057 ± 0.8912
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
0.5178 ± 0.1858
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
2.0965 ± 0.7155
Cross-validation details (10-fold Crossvalidation)