Run
48939

Run 48939

Task 23 (Supervised Classification) balance-scale Uploaded 09-04-2021 by Continuous Integration
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(num=sklearn.preprocessing.data.StandardScaler,cat=sk learn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm.classes.SVC)(8 )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``.
sklearn.preprocessing.data.StandardScaler(16)_copytrue
sklearn.preprocessing.data.StandardScaler(16)_with_meantrue
sklearn.preprocessing.data.StandardScaler(16)_with_stdtrue
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing.data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm.classes.SVC)(8)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing.data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm.classes.SVC)(8)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}]
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing.data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm.classes.SVC)(8)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing.data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(8)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing.data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(8)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing.data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(8)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing.data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(8)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing.data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(8)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "num", "step_name": "num", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}]
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing.data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(8)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(30)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(30)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(30)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(30)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(30)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(30)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(30)_sparsetrue
sklearn.svm.classes.SVC(8)_C1.0
sklearn.svm.classes.SVC(8)_cache_size200
sklearn.svm.classes.SVC(8)_class_weightnull
sklearn.svm.classes.SVC(8)_coef00.0
sklearn.svm.classes.SVC(8)_decision_function_shape"ovr"
sklearn.svm.classes.SVC(8)_degree3
sklearn.svm.classes.SVC(8)_gamma"scale"
sklearn.svm.classes.SVC(8)_kernel"rbf"
sklearn.svm.classes.SVC(8)_max_iter-1
sklearn.svm.classes.SVC(8)_probabilityfalse
sklearn.svm.classes.SVC(8)_random_state1
sklearn.svm.classes.SVC(8)_shrinkingtrue
sklearn.svm.classes.SVC(8)_tol0.001
sklearn.svm.classes.SVC(8)_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.

16 Evaluation measures

0.7273
Per class
Cross-validation details (10% Holdout set)
0.3518
Cross-validation details (10% Holdout set)
0.2956
Cross-validation details (10% Holdout set)
0.3107
Cross-validation details (10% Holdout set)
0.3857
Cross-validation details (10% Holdout set)
0.534
Cross-validation details (10% Holdout set)
206
Per class
Cross-validation details (10% Holdout set)
0.534
Cross-validation details (10% Holdout set)
1.3777
Cross-validation details (10% Holdout set)
0.8054
Cross-validation details (10% Holdout set)
0.4424
Cross-validation details (10% Holdout set)
0.5574
Cross-validation details (10% Holdout set)
1.26
Cross-validation details (10% Holdout set)
0.5894
Cross-validation details (10% Holdout set)