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Run 32018

Task 96 (Supervised Classification) credit-a Uploaded 30-03-2021 by Continuous Integration
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Flow

TESTab990a0f6esklearn.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``.
TESTab990a0f6esklearn.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
TESTab990a0f6esklearn.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"}}]
TESTab990a0f6esklearn.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
TESTab990a0f6esklearn.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
TESTab990a0f6esklearn.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"
TESTab990a0f6esklearn.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
TESTab990a0f6esklearn.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
TESTab990a0f6esklearn.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": [1, 2, 7, 10, 13, 14]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": [0, 3, 4, 5, 6, 8, 9, 11, 12]}}]
TESTab990a0f6esklearn.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
TESTab990a0f6esklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_memorynull
TESTab990a0f6esklearn.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"}}]
TESTab990a0f6esklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler)(1)_verbosefalse
TESTab990a0f6esklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTab990a0f6esklearn.impute._base.SimpleImputer(1)_copytrue
TESTab990a0f6esklearn.impute._base.SimpleImputer(1)_fill_valuenull
TESTab990a0f6esklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTab990a0f6esklearn.impute._base.SimpleImputer(1)_strategy"mean"
TESTab990a0f6esklearn.impute._base.SimpleImputer(1)_verbose0
TESTab990a0f6esklearn.preprocessing._data.StandardScaler(1)_copytrue
TESTab990a0f6esklearn.preprocessing._data.StandardScaler(1)_with_meantrue
TESTab990a0f6esklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
TESTab990a0f6esklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
TESTab990a0f6esklearn.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"}}]
TESTab990a0f6esklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
TESTab990a0f6eopenml.testing.CustomImputer(1)_add_indicatorfalse
TESTab990a0f6eopenml.testing.CustomImputer(1)_copytrue
TESTab990a0f6eopenml.testing.CustomImputer(1)_fill_valuenull
TESTab990a0f6eopenml.testing.CustomImputer(1)_missing_valuesNaN
TESTab990a0f6eopenml.testing.CustomImputer(1)_strategy"most_frequent"
TESTab990a0f6eopenml.testing.CustomImputer(1)_verbose0
TESTab990a0f6esklearn.preprocessing._encoders.OneHotEncoder(1)_categories"auto"
TESTab990a0f6esklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
TESTab990a0f6esklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
TESTab990a0f6esklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
TESTab990a0f6esklearn.preprocessing._encoders.OneHotEncoder(1)_sparsetrue
TESTab990a0f6esklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TESTab990a0f6esklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TESTab990a0f6esklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TESTab990a0f6esklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TESTab990a0f6esklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TESTab990a0f6esklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TESTab990a0f6esklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TESTab990a0f6esklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TESTab990a0f6esklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TESTab990a0f6esklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TESTab990a0f6esklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TESTab990a0f6esklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TESTab990a0f6esklearn.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

0.8288
Per class
Cross-validation details (33% Holdout set)
0.8283
Per class
Cross-validation details (33% Holdout set)
0.6564
Cross-validation details (33% Holdout set)
0.6542
Cross-validation details (33% Holdout set)
0.1718
Cross-validation details (33% Holdout set)
0.4978
Cross-validation details (33% Holdout set)
0.8282
Cross-validation details (33% Holdout set)
227
Per class
Cross-validation details (33% Holdout set)
0.8291
Per class
Cross-validation details (33% Holdout set)
0.8282
Cross-validation details (33% Holdout set)
1.0024
Cross-validation details (33% Holdout set)
0.3451
Cross-validation details (33% Holdout set)
0.5008
Cross-validation details (33% Holdout set)
0.4145
Cross-validation details (33% Holdout set)
0.8276
Cross-validation details (33% Holdout set)
0.8288
Cross-validation details (33% Holdout set)