Run
17010

Run 17010

Task 23 (Supervised Classification) balance-scale Uploaded 29-09-2024 by Continuous Integration
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=s klearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm._classes.SVC) (12)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(12)_copytrue
sklearn.preprocessing._data.StandardScaler(12)_with_meantrue
sklearn.preprocessing._data.StandardScaler(12)_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)(12)_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)(12)_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)(12)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(12)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(12)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(12)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(12)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(12)_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)(12)_verbosefalse
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(12)_verbose_feature_names_outtrue
sklearn.preprocessing._encoders.OneHotEncoder(12)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(12)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(12)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(12)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(12)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(12)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(12)_sparsetrue
sklearn.svm._classes.SVC(12)_C1.0
sklearn.svm._classes.SVC(12)_break_tiesfalse
sklearn.svm._classes.SVC(12)_cache_size200
sklearn.svm._classes.SVC(12)_class_weightnull
sklearn.svm._classes.SVC(12)_coef00.0
sklearn.svm._classes.SVC(12)_decision_function_shape"ovr"
sklearn.svm._classes.SVC(12)_degree3
sklearn.svm._classes.SVC(12)_gamma"scale"
sklearn.svm._classes.SVC(12)_kernel"rbf"
sklearn.svm._classes.SVC(12)_max_iter-1
sklearn.svm._classes.SVC(12)_probabilityfalse
sklearn.svm._classes.SVC(12)_random_state1
sklearn.svm._classes.SVC(12)_shrinkingtrue
sklearn.svm._classes.SVC(12)_tol0.001
sklearn.svm._classes.SVC(12)_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