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31765

Run 31765

Task 96 (Supervised Classification) credit-a Uploaded 30-03-2021 by Continuous Integration
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TESTa3931c1f50sklearn.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.VarianceThre shold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimato r=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``.
TESTa3931c1f50sklearn.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
TESTa3931c1f50sklearn.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"}}]
TESTa3931c1f50sklearn.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
TESTa3931c1f50sklearn.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
TESTa3931c1f50sklearn.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"
TESTa3931c1f50sklearn.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
TESTa3931c1f50sklearn.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
TESTa3931c1f50sklearn.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"}}}]
TESTa3931c1f50sklearn.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
TESTa3931c1f50sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
TESTa3931c1f50sklearn.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"}}]
TESTa3931c1f50sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
TESTa3931c1f50sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTa3931c1f50sklearn.impute._base.SimpleImputer(1)_copytrue
TESTa3931c1f50sklearn.impute._base.SimpleImputer(1)_fill_valuenull
TESTa3931c1f50sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTa3931c1f50sklearn.impute._base.SimpleImputer(1)_strategy"most_frequent"
TESTa3931c1f50sklearn.impute._base.SimpleImputer(1)_verbose0
TESTa3931c1f50sklearn.preprocessing._encoders.OneHotEncoder(1)_categorical_featuresnull
TESTa3931c1f50sklearn.preprocessing._encoders.OneHotEncoder(1)_categoriesnull
TESTa3931c1f50sklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
TESTa3931c1f50sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
TESTa3931c1f50sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
TESTa3931c1f50sklearn.preprocessing._encoders.OneHotEncoder(1)_n_valuesnull
TESTa3931c1f50sklearn.preprocessing._encoders.OneHotEncoder(1)_sparsetrue
TESTa3931c1f50sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memorynull
TESTa3931c1f50sklearn.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"}}]
TESTa3931c1f50sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_verbosefalse
TESTa3931c1f50openml.testing.CustomImputer(1)_add_indicatorfalse
TESTa3931c1f50openml.testing.CustomImputer(1)_copytrue
TESTa3931c1f50openml.testing.CustomImputer(1)_fill_valuenull
TESTa3931c1f50openml.testing.CustomImputer(1)_missing_valuesNaN
TESTa3931c1f50openml.testing.CustomImputer(1)_strategy"mean"
TESTa3931c1f50openml.testing.CustomImputer(1)_verbose0
TESTa3931c1f50sklearn.preprocessing.data.StandardScaler(1)_copytrue
TESTa3931c1f50sklearn.preprocessing.data.StandardScaler(1)_with_meantrue
TESTa3931c1f50sklearn.preprocessing.data.StandardScaler(1)_with_stdtrue
TESTa3931c1f50sklearn.feature_selection.variance_threshold.VarianceThreshold(1)_threshold0.0
TESTa3931c1f50sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier)(1)_cv3
TESTa3931c1f50sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier)(1)_error_score"raise-deprecating"
TESTa3931c1f50sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier)(1)_iid"warn"
TESTa3931c1f50sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier)(1)_n_iter10
TESTa3931c1f50sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier)(1)_n_jobsnull
TESTa3931c1f50sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier)(1)_param_distributions{"n_neighbors": [2, 3, 4, 5, 6, 7, 8, 9]}
TESTa3931c1f50sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier)(1)_pre_dispatch"2*n_jobs"
TESTa3931c1f50sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier)(1)_random_state62501
TESTa3931c1f50sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier)(1)_refittrue
TESTa3931c1f50sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier)(1)_return_train_scorefalse
TESTa3931c1f50sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier)(1)_scoringnull
TESTa3931c1f50sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier)(1)_verbose0
TESTa3931c1f50sklearn.neighbors.classification.KNeighborsClassifier(1)_algorithm"auto"
TESTa3931c1f50sklearn.neighbors.classification.KNeighborsClassifier(1)_leaf_size30
TESTa3931c1f50sklearn.neighbors.classification.KNeighborsClassifier(1)_metric"minkowski"
TESTa3931c1f50sklearn.neighbors.classification.KNeighborsClassifier(1)_metric_paramsnull
TESTa3931c1f50sklearn.neighbors.classification.KNeighborsClassifier(1)_n_jobsnull
TESTa3931c1f50sklearn.neighbors.classification.KNeighborsClassifier(1)_n_neighbors5
TESTa3931c1f50sklearn.neighbors.classification.KNeighborsClassifier(1)_p2
TESTa3931c1f50sklearn.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)