Run
10560578

Run 10560578

Task 167141 (Supervised Classification) churn Uploaded 14-08-2021 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_state17190
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

0.5071 ± 0.0106
Per class
Cross-validation details (10-fold Crossvalidation)
0.7978 ± 0.0068
Per class
Cross-validation details (10-fold Crossvalidation)
0.0238 ± 0.0342
Cross-validation details (10-fold Crossvalidation)
0.2686 ± 0.0162
Cross-validation details (10-fold Crossvalidation)
0.1414 ± 0.003
Cross-validation details (10-fold Crossvalidation)
0.2429 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
0.8586 ± 0.003
Cross-validation details (10-fold Crossvalidation)
5000
Per class
Cross-validation details (10-fold Crossvalidation)
0.8094 ± 0.0565
Per class
Cross-validation details (10-fold Crossvalidation)
0.8586 ± 0.003
Cross-validation details (10-fold Crossvalidation)
0.5879 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
0.5821 ± 0.0126
Cross-validation details (10-fold Crossvalidation)
0.3484 ± 0.001
Cross-validation details (10-fold Crossvalidation)
0.376 ± 0.004
Cross-validation details (10-fold Crossvalidation)
1.0792 ± 0.0122
Cross-validation details (10-fold Crossvalidation)
0.5071 ± 0.0106
Cross-validation details (10-fold Crossvalidation)