Run
9376

Run 9376

Task 119 (Supervised Classification) diabetes Uploaded 25-11-2022 by Continuous Integration
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TESTc030b89f2csklearn.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``.
TESTc030b89f2csklearn.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
TESTc030b89f2csklearn.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"}}]
TESTc030b89f2csklearn.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
TESTc030b89f2csklearn.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
TESTc030b89f2csklearn.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"
TESTc030b89f2csklearn.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
TESTc030b89f2csklearn.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
TESTc030b89f2csklearn.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": []}}]
TESTc030b89f2csklearn.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
TESTc030b89f2csklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memorynull
TESTc030b89f2csklearn.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"}}]
TESTc030b89f2csklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbosefalse
TESTc030b89f2csklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTc030b89f2csklearn.impute._base.SimpleImputer(1)_copytrue
TESTc030b89f2csklearn.impute._base.SimpleImputer(1)_fill_valuenull
TESTc030b89f2csklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTc030b89f2csklearn.impute._base.SimpleImputer(1)_strategy"mean"
TESTc030b89f2csklearn.impute._base.SimpleImputer(1)_verbose0
TESTc030b89f2csklearn.preprocessing._data.StandardScaler(1)_copytrue
TESTc030b89f2csklearn.preprocessing._data.StandardScaler(1)_with_meantrue
TESTc030b89f2csklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
TESTc030b89f2csklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
TESTc030b89f2csklearn.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"}}]
TESTc030b89f2csklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
TESTc030b89f2copenml.testing.CustomImputer(1)_add_indicatorfalse
TESTc030b89f2copenml.testing.CustomImputer(1)_copytrue
TESTc030b89f2copenml.testing.CustomImputer(1)_fill_valuenull
TESTc030b89f2copenml.testing.CustomImputer(1)_missing_valuesNaN
TESTc030b89f2copenml.testing.CustomImputer(1)_strategy"most_frequent"
TESTc030b89f2copenml.testing.CustomImputer(1)_verbose0
TESTc030b89f2csklearn.preprocessing._encoders.OneHotEncoder(1)_categories"auto"
TESTc030b89f2csklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
TESTc030b89f2csklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
TESTc030b89f2csklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
TESTc030b89f2csklearn.preprocessing._encoders.OneHotEncoder(1)_sparsetrue
TESTc030b89f2csklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TESTc030b89f2csklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TESTc030b89f2csklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TESTc030b89f2csklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TESTc030b89f2csklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TESTc030b89f2csklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TESTc030b89f2csklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TESTc030b89f2csklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TESTc030b89f2csklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TESTc030b89f2csklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TESTc030b89f2csklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TESTc030b89f2csklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TESTc030b89f2csklearn.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