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31888

Run 31888

Task 119 (Supervised Classification) diabetes Uploaded 30-03-2021 by Continuous Integration
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TEST5155e2b459sklearn.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``.
TEST5155e2b459sklearn.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
TEST5155e2b459sklearn.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"}}]
TEST5155e2b459sklearn.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
TEST5155e2b459sklearn.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
TEST5155e2b459sklearn.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"
TEST5155e2b459sklearn.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
TEST5155e2b459sklearn.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
TEST5155e2b459sklearn.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": []}}]
TEST5155e2b459sklearn.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
TEST5155e2b459sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memorynull
TEST5155e2b459sklearn.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"}}]
TEST5155e2b459sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbosefalse
TEST5155e2b459sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TEST5155e2b459sklearn.impute._base.SimpleImputer(1)_copytrue
TEST5155e2b459sklearn.impute._base.SimpleImputer(1)_fill_valuenull
TEST5155e2b459sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TEST5155e2b459sklearn.impute._base.SimpleImputer(1)_strategy"mean"
TEST5155e2b459sklearn.impute._base.SimpleImputer(1)_verbose0
TEST5155e2b459sklearn.preprocessing._data.StandardScaler(1)_copytrue
TEST5155e2b459sklearn.preprocessing._data.StandardScaler(1)_with_meantrue
TEST5155e2b459sklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
TEST5155e2b459sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
TEST5155e2b459sklearn.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"}}]
TEST5155e2b459sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
TEST5155e2b459openml.testing.CustomImputer(1)_add_indicatorfalse
TEST5155e2b459openml.testing.CustomImputer(1)_copytrue
TEST5155e2b459openml.testing.CustomImputer(1)_fill_valuenull
TEST5155e2b459openml.testing.CustomImputer(1)_missing_valuesNaN
TEST5155e2b459openml.testing.CustomImputer(1)_strategy"most_frequent"
TEST5155e2b459openml.testing.CustomImputer(1)_verbose0
TEST5155e2b459sklearn.preprocessing._encoders.OneHotEncoder(1)_categories"auto"
TEST5155e2b459sklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
TEST5155e2b459sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
TEST5155e2b459sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
TEST5155e2b459sklearn.preprocessing._encoders.OneHotEncoder(1)_sparsetrue
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_presort"deprecated"
TEST5155e2b459sklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TEST5155e2b459sklearn.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

0.6988
Per class
Cross-validation details (10% Holdout set)
0.7227
Per class
Cross-validation details (10% Holdout set)
0.3994
Cross-validation details (10% Holdout set)
0.3751
Cross-validation details (10% Holdout set)
0.2767
Cross-validation details (10% Holdout set)
0.4589
Cross-validation details (10% Holdout set)
0.7233
Cross-validation details (10% Holdout set)
253
Per class
Cross-validation details (10% Holdout set)
0.7221
Per class
Cross-validation details (10% Holdout set)
0.7233
Cross-validation details (10% Holdout set)
0.9463
Cross-validation details (10% Holdout set)
0.6029
Cross-validation details (10% Holdout set)
0.4813
Cross-validation details (10% Holdout set)
0.526
Cross-validation details (10% Holdout set)
1.093
Cross-validation details (10% Holdout set)
0.6988
Cross-validation details (10% Holdout set)