Run
10560579

Run 10560579

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)),extratreesclassifier=sk learn.ensemble.forest.ExtraTreesClassifier)(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)),extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(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)),extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "extratreesclassifier", "step_name": "extratreesclassifier"}}]
sklearn.ensemble.forest.ExtraTreesClassifier(17)_bootstraptrue
sklearn.ensemble.forest.ExtraTreesClassifier(17)_class_weightnull
sklearn.ensemble.forest.ExtraTreesClassifier(17)_criterion"gini"
sklearn.ensemble.forest.ExtraTreesClassifier(17)_max_depthnull
sklearn.ensemble.forest.ExtraTreesClassifier(17)_max_features0.033585105520519076
sklearn.ensemble.forest.ExtraTreesClassifier(17)_max_leaf_nodesnull
sklearn.ensemble.forest.ExtraTreesClassifier(17)_min_impurity_decrease0.0
sklearn.ensemble.forest.ExtraTreesClassifier(17)_min_impurity_splitnull
sklearn.ensemble.forest.ExtraTreesClassifier(17)_min_samples_leaf5
sklearn.ensemble.forest.ExtraTreesClassifier(17)_min_samples_split10
sklearn.ensemble.forest.ExtraTreesClassifier(17)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.ExtraTreesClassifier(17)_n_estimators100
sklearn.ensemble.forest.ExtraTreesClassifier(17)_n_jobsnull
sklearn.ensemble.forest.ExtraTreesClassifier(17)_oob_scorefalse
sklearn.ensemble.forest.ExtraTreesClassifier(17)_random_state13682
sklearn.ensemble.forest.ExtraTreesClassifier(17)_verbose0
sklearn.ensemble.forest.ExtraTreesClassifier(17)_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.

16 Evaluation measures

0.6876 ± 0.0424
Per class
Cross-validation details (10-fold Crossvalidation)
-0.0032 ± 0.0188
Cross-validation details (10-fold Crossvalidation)
0.2364 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
0.2429 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
0.8586 ± 0.001
Cross-validation details (10-fold Crossvalidation)
5000
Per class
Cross-validation details (10-fold Crossvalidation)
0.8586 ± 0.001
Cross-validation details (10-fold Crossvalidation)
0.5879 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
0.9731 ± 0.0067
Cross-validation details (10-fold Crossvalidation)
0.3484 ± 0.001
Cross-validation details (10-fold Crossvalidation)
0.3402 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
0.9764 ± 0.0061
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)