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Task 23 (Supervised Classification) balance-scale Uploaded 18-10-2024 by Continuous Integration
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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) (10)	A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final :term:`predictor` for predictive modeling. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final :term:`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:`~s...
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm._classes.SVC)(10)_memory	null
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm._classes.SVC)(10)_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)(10)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(10)_force_int_remainder_cols	true
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(10)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(10)_remainder	"drop"
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(10)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(10)_transformer_weights	null
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(10)_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)(10)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder)(10)_verbose_feature_names_out	true
sklearn.preprocessing._data.StandardScaler(10)_copy	true
sklearn.preprocessing._data.StandardScaler(10)_with_mean	true
sklearn.preprocessing._data.StandardScaler(10)_with_std	true
sklearn.preprocessing._encoders.OneHotEncoder(10)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(10)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(10)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(10)_feature_name_combiner	"concat"
sklearn.preprocessing._encoders.OneHotEncoder(10)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(10)_max_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(10)_min_frequency	null
sklearn.preprocessing._encoders.OneHotEncoder(10)_sparse_output	true
sklearn.svm._classes.SVC(10)_C	1.0
sklearn.svm._classes.SVC(10)_break_ties	false
sklearn.svm._classes.SVC(10)_cache_size	200
sklearn.svm._classes.SVC(10)_class_weight	null
sklearn.svm._classes.SVC(10)_coef0	0.0
sklearn.svm._classes.SVC(10)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(10)_degree	3
sklearn.svm._classes.SVC(10)_gamma	"scale"
sklearn.svm._classes.SVC(10)_kernel	"rbf"
sklearn.svm._classes.SVC(10)_max_iter	-1
sklearn.svm._classes.SVC(10)_probability	false
sklearn.svm._classes.SVC(10)_random_state	1
sklearn.svm._classes.SVC(10)_shrinking	true
sklearn.svm._classes.SVC(10)_tol	0.001
sklearn.svm._classes.SVC(10)_verbose	false