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32099

Run 32099

Task 96 (Supervised Classification) credit-a Uploaded 30-03-2021 by Continuous Integration
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TEST6e6c3ae445sklearn.pipeline.Pipeline(Imputer=sklearn.compose._column_tra nsformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=skle arn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoder s.OneHotEncoder),cont=sklearn.pipeline.Pipeline(customimputer=openml.testin g.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)) ,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceTh reshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estima tor=sklearn.neighbors._classification.KNeighborsClassifier))(1)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 it to 'passthrough' or ``None``.
TEST6e6c3ae445sklearn.pipeline.Pipeline(Imputer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)),VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier))(1)_memorynull
TEST6e6c3ae445sklearn.pipeline.Pipeline(Imputer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)),VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier))(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "VarianceThreshold", "step_name": "VarianceThreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Estimator", "step_name": "Estimator"}}]
TEST6e6c3ae445sklearn.pipeline.Pipeline(Imputer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)),VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier))(1)_verbosefalse
TEST6e6c3ae445sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_n_jobsnull
TEST6e6c3ae445sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_remainder"drop"
TEST6e6c3ae445sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_sparse_threshold0.3
TEST6e6c3ae445sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_transformer_weightsnull
TEST6e6c3ae445sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cont", "step_name": "cont", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}]
TEST6e6c3ae445sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_verbosefalse
TEST6e6c3ae445sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
TEST6e6c3ae445sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_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"}}]
TEST6e6c3ae445sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
TEST6e6c3ae445sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TEST6e6c3ae445sklearn.impute._base.SimpleImputer(1)_copytrue
TEST6e6c3ae445sklearn.impute._base.SimpleImputer(1)_fill_valuenull
TEST6e6c3ae445sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TEST6e6c3ae445sklearn.impute._base.SimpleImputer(1)_strategy"most_frequent"
TEST6e6c3ae445sklearn.impute._base.SimpleImputer(1)_verbose0
TEST6e6c3ae445sklearn.preprocessing._encoders.OneHotEncoder(1)_categories"auto"
TEST6e6c3ae445sklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
TEST6e6c3ae445sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
TEST6e6c3ae445sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
TEST6e6c3ae445sklearn.preprocessing._encoders.OneHotEncoder(1)_sparsetrue
TEST6e6c3ae445sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memorynull
TEST6e6c3ae445sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "customimputer", "step_name": "customimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
TEST6e6c3ae445sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbosefalse
TEST6e6c3ae445openml.testing.CustomImputer(1)_add_indicatorfalse
TEST6e6c3ae445openml.testing.CustomImputer(1)_copytrue
TEST6e6c3ae445openml.testing.CustomImputer(1)_fill_valuenull
TEST6e6c3ae445openml.testing.CustomImputer(1)_missing_valuesNaN
TEST6e6c3ae445openml.testing.CustomImputer(1)_strategy"mean"
TEST6e6c3ae445openml.testing.CustomImputer(1)_verbose0
TEST6e6c3ae445sklearn.preprocessing._data.StandardScaler(1)_copytrue
TEST6e6c3ae445sklearn.preprocessing._data.StandardScaler(1)_with_meantrue
TEST6e6c3ae445sklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
TEST6e6c3ae445sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold0.0
TEST6e6c3ae445sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier)(1)_cv3
TEST6e6c3ae445sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier)(1)_error_scoreNaN
TEST6e6c3ae445sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier)(1)_iid"deprecated"
TEST6e6c3ae445sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier)(1)_n_iter10
TEST6e6c3ae445sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier)(1)_n_jobsnull
TEST6e6c3ae445sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier)(1)_param_distributions{"n_neighbors": [2, 3, 4, 5, 6, 7, 8, 9]}
TEST6e6c3ae445sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier)(1)_pre_dispatch"2*n_jobs"
TEST6e6c3ae445sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier)(1)_random_state62501
TEST6e6c3ae445sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier)(1)_refittrue
TEST6e6c3ae445sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier)(1)_return_train_scorefalse
TEST6e6c3ae445sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier)(1)_scoringnull
TEST6e6c3ae445sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors._classification.KNeighborsClassifier)(1)_verbose0
TEST6e6c3ae445sklearn.neighbors._classification.KNeighborsClassifier(1)_algorithm"auto"
TEST6e6c3ae445sklearn.neighbors._classification.KNeighborsClassifier(1)_leaf_size30
TEST6e6c3ae445sklearn.neighbors._classification.KNeighborsClassifier(1)_metric"minkowski"
TEST6e6c3ae445sklearn.neighbors._classification.KNeighborsClassifier(1)_metric_paramsnull
TEST6e6c3ae445sklearn.neighbors._classification.KNeighborsClassifier(1)_n_jobsnull
TEST6e6c3ae445sklearn.neighbors._classification.KNeighborsClassifier(1)_n_neighbors5
TEST6e6c3ae445sklearn.neighbors._classification.KNeighborsClassifier(1)_p2
TEST6e6c3ae445sklearn.neighbors._classification.KNeighborsClassifier(1)_weights"uniform"

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

0.8968
Per class
Cross-validation details (33% Holdout set)
0.8458
Per class
Cross-validation details (33% Holdout set)
0.6913
Cross-validation details (33% Holdout set)
0.563
Cross-validation details (33% Holdout set)
0.2283
Cross-validation details (33% Holdout set)
0.4978
Cross-validation details (33% Holdout set)
0.8458
Cross-validation details (33% Holdout set)
227
Per class
Cross-validation details (33% Holdout set)
0.8459
Per class
Cross-validation details (33% Holdout set)
0.8458
Cross-validation details (33% Holdout set)
1.0024
Cross-validation details (33% Holdout set)
0.4587
Cross-validation details (33% Holdout set)
0.5008
Cross-validation details (33% Holdout set)
0.3528
Cross-validation details (33% Holdout set)
0.7044
Cross-validation details (33% Holdout set)
0.8457
Cross-validation details (33% Holdout set)