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9315

Run 9315

Task 96 (Supervised Classification) credit-a Uploaded 25-11-2022 by Continuous Integration
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TEST0e12334d9csklearn.pipeline.Pipeline(transformer=sklearn.compose._column _transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimpu ter=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing ._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=open ml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneH otEncoder)),classifier=sklearn.tree._classes.DecisionTreeClassifier)(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``.
TEST0e12334d9csklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_memorynull
TEST0e12334d9csklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "transformer", "step_name": "transformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
TEST0e12334d9csklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbosefalse
TEST0e12334d9csklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_n_jobsnull
TEST0e12334d9csklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_remainder"passthrough"
TEST0e12334d9csklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_sparse_threshold0.3
TEST0e12334d9csklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_transformer_weightsnull
TEST0e12334d9csklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [1, 2, 7, 10, 13, 14]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": [0, 3, 4, 5, 6, 8, 9, 11, 12]}}]
TEST0e12334d9csklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_verbosefalse
TEST0e12334d9csklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memorynull
TEST0e12334d9csklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
TEST0e12334d9csklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbosefalse
TEST0e12334d9csklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TEST0e12334d9csklearn.impute._base.SimpleImputer(1)_copytrue
TEST0e12334d9csklearn.impute._base.SimpleImputer(1)_fill_valuenull
TEST0e12334d9csklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TEST0e12334d9csklearn.impute._base.SimpleImputer(1)_strategy"mean"
TEST0e12334d9csklearn.impute._base.SimpleImputer(1)_verbose0
TEST0e12334d9csklearn.preprocessing._data.StandardScaler(1)_copytrue
TEST0e12334d9csklearn.preprocessing._data.StandardScaler(1)_with_meantrue
TEST0e12334d9csklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
TEST0e12334d9csklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
TEST0e12334d9csklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "customimputer", "step_name": "customimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
TEST0e12334d9csklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
TEST0e12334d9copenml.testing.CustomImputer(1)_add_indicatorfalse
TEST0e12334d9copenml.testing.CustomImputer(1)_copytrue
TEST0e12334d9copenml.testing.CustomImputer(1)_fill_valuenull
TEST0e12334d9copenml.testing.CustomImputer(1)_missing_valuesNaN
TEST0e12334d9copenml.testing.CustomImputer(1)_strategy"most_frequent"
TEST0e12334d9copenml.testing.CustomImputer(1)_verbose0
TEST0e12334d9csklearn.preprocessing._encoders.OneHotEncoder(1)_categories"auto"
TEST0e12334d9csklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
TEST0e12334d9csklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
TEST0e12334d9csklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
TEST0e12334d9csklearn.preprocessing._encoders.OneHotEncoder(1)_sparsetrue
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TEST0e12334d9csklearn.tree._classes.DecisionTreeClassifier(1)_splitter"best"

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