Run
18054

Run 18054

Task 96 (Supervised Classification) credit-a Uploaded 12-11-2019 by Continuous Integration
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  • openml-python Sklearn_0.22.dev0.
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Flow

TESTecfa1cb70fsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.Simple Imputer,transformer=sklearn.compose._column_transformer.ColumnTransformer(n umeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preproces sing._encoders.OneHotEncoder),classifier=sklearn.tree._classes.DecisionTree Classifier)(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``.
TESTecfa1cb70fsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_memorynull
TESTecfa1cb70fsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "transformer", "step_name": "transformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
TESTecfa1cb70fsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbosefalse
TESTecfa1cb70fsklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTecfa1cb70fsklearn.impute._base.SimpleImputer(1)_copytrue
TESTecfa1cb70fsklearn.impute._base.SimpleImputer(1)_fill_value-1
TESTecfa1cb70fsklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTecfa1cb70fsklearn.impute._base.SimpleImputer(1)_strategy"constant"
TESTecfa1cb70fsklearn.impute._base.SimpleImputer(1)_verbose0
TESTecfa1cb70fsklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobsnull
TESTecfa1cb70fsklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder"passthrough"
TESTecfa1cb70fsklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold0.3
TESTecfa1cb70fsklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weightsnull
TESTecfa1cb70fsklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 3, 4, 5, 6, 8, 9, 11, 12]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": [1, 2, 7, 10, 13, 14]}}]
TESTecfa1cb70fsklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
TESTecfa1cb70fsklearn.preprocessing._data.StandardScaler(1)_copytrue
TESTecfa1cb70fsklearn.preprocessing._data.StandardScaler(1)_with_meantrue
TESTecfa1cb70fsklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
TESTecfa1cb70fsklearn.preprocessing._encoders.OneHotEncoder(1)_categories"auto"
TESTecfa1cb70fsklearn.preprocessing._encoders.OneHotEncoder(1)_dropnull
TESTecfa1cb70fsklearn.preprocessing._encoders.OneHotEncoder(1)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
TESTecfa1cb70fsklearn.preprocessing._encoders.OneHotEncoder(1)_handle_unknown"ignore"
TESTecfa1cb70fsklearn.preprocessing._encoders.OneHotEncoder(1)_sparsetrue
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_presort"deprecated"
TESTecfa1cb70fsklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TESTecfa1cb70fsklearn.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

0.8521
Per class
Cross-validation details (33% Holdout set)
0.8501
Per class
Cross-validation details (33% Holdout set)
0.7012
Cross-validation details (33% Holdout set)
0.6986
Cross-validation details (33% Holdout set)
0.1498
Cross-validation details (33% Holdout set)
0.4978
Cross-validation details (33% Holdout set)
0.8502
Cross-validation details (33% Holdout set)
227
Per class
Cross-validation details (33% Holdout set)
0.8548
Per class
Cross-validation details (33% Holdout set)
0.8502
Cross-validation details (33% Holdout set)
1.0024
Cross-validation details (33% Holdout set)
0.3009
Cross-validation details (33% Holdout set)
0.5008
Cross-validation details (33% Holdout set)
0.387
Cross-validation details (33% Holdout set)
0.7727
Cross-validation details (33% Holdout set)
0.8521
Cross-validation details (33% Holdout set)