Run
10560712

Run 10560712

Task 9985 (Supervised Classification) first-order-theorem-proving Uploaded 21-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=s klearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imp uter,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=skle arn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotenco der=sklearn.preprocessing._encoders.OneHotEncoder)),sgdclassifier=sklearn.l inear_model.stochastic_gradient.SGDClassifier)(2)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting to None.
sklearn.preprocessing.imputation.Imputer(56)_axis0
sklearn.preprocessing.imputation.Imputer(56)_copytrue
sklearn.preprocessing.imputation.Imputer(56)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(56)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(56)_verbose0
sklearn.preprocessing.data.StandardScaler(44)_copytrue
sklearn.preprocessing.data.StandardScaler(44)_with_meantrue
sklearn.preprocessing.data.StandardScaler(44)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(7)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(7)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
sklearn.impute.SimpleImputer(19)_copytrue
sklearn.impute.SimpleImputer(19)_fill_value-1
sklearn.impute.SimpleImputer(19)_missing_valuesNaN
sklearn.impute.SimpleImputer(19)_strategy"constant"
sklearn.impute.SimpleImputer(19)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(28)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(28)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(28)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(28)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(28)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(28)_sparsetrue
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(3)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}]
sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(3)_memorynull
sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(3)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "missingindicator", "step_name": "missingindicator"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
sklearn.impute.MissingIndicator(4)_error_on_newfalse
sklearn.impute.MissingIndicator(4)_features"missing-only"
sklearn.impute.MissingIndicator(4)_missing_valuesNaN
sklearn.impute.MissingIndicator(4)_sparse"auto"
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),sgdclassifier=sklearn.linear_model.stochastic_gradient.SGDClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),sgdclassifier=sklearn.linear_model.stochastic_gradient.SGDClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "sgdclassifier", "step_name": "sgdclassifier"}}]
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_alpha3.8875083608209314e-05
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_averagetrue
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_class_weightnull
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_early_stoppingfalse
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_epsilon0.1
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_eta00.0
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_fit_intercepttrue
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_l1_ratio0.15
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_learning_rate"optimal"
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_loss"hinge"
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_max_iternull
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_n_iternull
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_n_iter_no_change5
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_n_jobsnull
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_penalty"l2"
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_power_t0.5
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_random_state36822
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_shuffletrue
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_tol4.896672457206874e-05
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_validation_fraction0.1
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_verbose0
sklearn.linear_model.stochastic_gradient.SGDClassifier(11)_warm_startfalse

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.

18 Evaluation measures

0.6091 ± 0.016
Per class
Cross-validation details (10-fold Crossvalidation)
0.4179 ± 0.018
Per class
Cross-validation details (10-fold Crossvalidation)
0.2202 ± 0.0283
Cross-validation details (10-fold Crossvalidation)
0.2798 ± 0.0178
Cross-validation details (10-fold Crossvalidation)
0.1786 ± 0.0054
Cross-validation details (10-fold Crossvalidation)
0.2508 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.4642 ± 0.0161
Cross-validation details (10-fold Crossvalidation)
6118
Per class
Cross-validation details (10-fold Crossvalidation)
0.4225 ± 0.018
Per class
Cross-validation details (10-fold Crossvalidation)
0.4642 ± 0.0161
Cross-validation details (10-fold Crossvalidation)
2.3 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
0.7122 ± 0.0211
Cross-validation details (10-fold Crossvalidation)
0.3541 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.4226 ± 0.0064
Cross-validation details (10-fold Crossvalidation)
1.1936 ± 0.0177
Cross-validation details (10-fold Crossvalidation)
0.2994 ± 0.016
Cross-validation details (10-fold Crossvalidation)