Run
1370

Run 1370

Task 1 (Supervised Classification) anneal Uploaded 11-01-2024 by Continuous Integration
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sklearn.pipeline.Pipeline(preprocess=sklearn.compose._column_transformer.Co lumnTransformer(cat=sklearn.preprocessing._encoders.OneHotEncoder,cont=skle arn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,stan dardscaler=sklearn.preprocessing._data.StandardScaler)),estimator=sklearn.t ree._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...
sklearn.preprocessing._data.StandardScaler(1)_copytrue
sklearn.preprocessing._data.StandardScaler(1)_with_meantrue
sklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(1)_random_state56681
sklearn.tree._classes.DecisionTreeClassifier(1)_splitter"best"
sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(1)_copytrue
sklearn.impute._base.SimpleImputer(1)_fill_valuenull
sklearn.impute._base.SimpleImputer(1)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(1)_strategy"median"
sklearn.pipeline.Pipeline(preprocess=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.preprocessing._encoders.OneHotEncoder,cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)),estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(preprocess=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.preprocessing._encoders.OneHotEncoder,cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)),estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "preprocess", "step_name": "preprocess"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(preprocess=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.preprocessing._encoders.OneHotEncoder,cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)),estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.preprocessing._encoders.OneHotEncoder,cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.preprocessing._encoders.OneHotEncoder,cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.preprocessing._encoders.OneHotEncoder,cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.preprocessing._encoders.OneHotEncoder,cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.preprocessing._encoders.OneHotEncoder,cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cont", "step_name": "cont", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}]
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.preprocessing._encoders.OneHotEncoder,cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.preprocessing._encoders.OneHotEncoder,cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler))(1)_verbose_feature_names_outtrue
sklearn.preprocessing._encoders.OneHotEncoder(1)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(1)_feature_name_combiner"concat"
sklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(1)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(1)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(1)_sparse"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(1)_sparse_outputtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memorynull
sklearn.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"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbosefalse

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.

17 Evaluation measures

0.9895 ± 0.0114
Per class
0.9932 ± 0.008
Per class
0.9832 ± 0.0192
0.9745 ± 0.0274
0.0022 ± 0.0026
0.1343 ± 0.0012
0.9933 ± 0.0078
898
Per class
0.9933 ± 0.0065
Per class
0.9933 ± 0.0078
1.1915 ± 0.0248
0.0166 ± 0.0194
0.2582 ± 0.0024
0.0472 ± 0.0356
0.1828 ± 0.1377