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9903

Run 9903

Task 119 (Supervised Classification) diabetes Uploaded 28-11-2022 by Continuous Integration
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Flow

TEST6250eb97dasklearn.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``.
TEST6250eb97dasklearn.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
TEST6250eb97dasklearn.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"}}]
TEST6250eb97dasklearn.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
TEST6250eb97dasklearn.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
TEST6250eb97dasklearn.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"
TEST6250eb97dasklearn.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
TEST6250eb97dasklearn.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
TEST6250eb97dasklearn.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": [0, 1, 2, 3, 4, 5, 6, 7]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}]
TEST6250eb97dasklearn.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
TEST6250eb97dasklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memorynull
TEST6250eb97dasklearn.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"}}]
TEST6250eb97dasklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbosefalse
TEST6250eb97dasklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TEST6250eb97dasklearn.impute._base.SimpleImputer(1)_copytrue
TEST6250eb97dasklearn.impute._base.SimpleImputer(1)_fill_valuenull
TEST6250eb97dasklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TEST6250eb97dasklearn.impute._base.SimpleImputer(1)_strategy"mean"
TEST6250eb97dasklearn.impute._base.SimpleImputer(1)_verbose0
TEST6250eb97dasklearn.preprocessing._data.StandardScaler(1)_copytrue
TEST6250eb97dasklearn.preprocessing._data.StandardScaler(1)_with_meantrue
TEST6250eb97dasklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
TEST6250eb97dasklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
TEST6250eb97dasklearn.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"}}]
TEST6250eb97dasklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
TEST6250eb97daopenml.testing.CustomImputer(1)_add_indicatorfalse
TEST6250eb97daopenml.testing.CustomImputer(1)_copytrue
TEST6250eb97daopenml.testing.CustomImputer(1)_fill_valuenull
TEST6250eb97daopenml.testing.CustomImputer(1)_missing_valuesNaN
TEST6250eb97daopenml.testing.CustomImputer(1)_strategy"most_frequent"
TEST6250eb97daopenml.testing.CustomImputer(1)_verbose0
TEST6250eb97dasklearn.preprocessing._encoders.OneHotEncoder(1)_categories"auto"
TEST6250eb97dasklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
TEST6250eb97dasklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
TEST6250eb97dasklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
TEST6250eb97dasklearn.preprocessing._encoders.OneHotEncoder(1)_sparsetrue
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_presort"deprecated"
TEST6250eb97dasklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TEST6250eb97dasklearn.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