Run
2364

Run 2364

Task 119 (Supervised Classification) diabetes Uploaded 12-01-2024 by Continuous Integration
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TEST7d94dc3422sklearn.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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl...
TEST7d94dc3422sklearn.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
TEST7d94dc3422sklearn.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"}}]
TEST7d94dc3422sklearn.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
TEST7d94dc3422sklearn.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
TEST7d94dc3422sklearn.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"
TEST7d94dc3422sklearn.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
TEST7d94dc3422sklearn.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
TEST7d94dc3422sklearn.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": []}}]
TEST7d94dc3422sklearn.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
TEST7d94dc3422sklearn.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)_verbose_feature_names_outtrue
TEST7d94dc3422sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memorynull
TEST7d94dc3422sklearn.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"}}]
TEST7d94dc3422sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbosefalse
TEST7d94dc3422sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TEST7d94dc3422sklearn.impute._base.SimpleImputer(1)_copytrue
TEST7d94dc3422sklearn.impute._base.SimpleImputer(1)_fill_valuenull
TEST7d94dc3422sklearn.impute._base.SimpleImputer(1)_keep_empty_featuresfalse
TEST7d94dc3422sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TEST7d94dc3422sklearn.impute._base.SimpleImputer(1)_strategy"mean"
TEST7d94dc3422sklearn.preprocessing._data.StandardScaler(1)_copytrue
TEST7d94dc3422sklearn.preprocessing._data.StandardScaler(1)_with_meantrue
TEST7d94dc3422sklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
TEST7d94dc3422sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
TEST7d94dc3422sklearn.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"}}]
TEST7d94dc3422sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
TEST7d94dc3422openml.testing.CustomImputer(1)_add_indicatorfalse
TEST7d94dc3422openml.testing.CustomImputer(1)_copytrue
TEST7d94dc3422openml.testing.CustomImputer(1)_fill_valuenull
TEST7d94dc3422openml.testing.CustomImputer(1)_keep_empty_featuresfalse
TEST7d94dc3422openml.testing.CustomImputer(1)_missing_valuesNaN
TEST7d94dc3422openml.testing.CustomImputer(1)_strategy"most_frequent"
TEST7d94dc3422sklearn.preprocessing._encoders.OneHotEncoder(1)_categories"auto"
TEST7d94dc3422sklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
TEST7d94dc3422sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
TEST7d94dc3422sklearn.preprocessing._encoders.OneHotEncoder(1)_feature_name_combiner"concat"
TEST7d94dc3422sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
TEST7d94dc3422sklearn.preprocessing._encoders.OneHotEncoder(1)_max_categoriesnull
TEST7d94dc3422sklearn.preprocessing._encoders.OneHotEncoder(1)_min_frequencynull
TEST7d94dc3422sklearn.preprocessing._encoders.OneHotEncoder(1)_sparse"deprecated"
TEST7d94dc3422sklearn.preprocessing._encoders.OneHotEncoder(1)_sparse_outputtrue
TEST7d94dc3422sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TEST7d94dc3422sklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TEST7d94dc3422sklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TEST7d94dc3422sklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TEST7d94dc3422sklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TEST7d94dc3422sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TEST7d94dc3422sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TEST7d94dc3422sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TEST7d94dc3422sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TEST7d94dc3422sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TEST7d94dc3422sklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TEST7d94dc3422sklearn.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