Run 10595175

Task 9978 (Supervised Classification) ozone-level-8hr Uploaded 30-11-2024 by José Evans
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sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer .ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler),classifi er=sklearn.svm._classes.SVC)(1)	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.preprocessing._data.StandardScaler(20)_copy	true
sklearn.preprocessing._data.StandardScaler(20)_with_mean	true
sklearn.preprocessing._data.StandardScaler(20)_with_std	true
sklearn.svm._classes.SVC(17)_C	1.0
sklearn.svm._classes.SVC(17)_break_ties	false
sklearn.svm._classes.SVC(17)_cache_size	200
sklearn.svm._classes.SVC(17)_class_weight	null
sklearn.svm._classes.SVC(17)_coef0	0.0
sklearn.svm._classes.SVC(17)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(17)_degree	3
sklearn.svm._classes.SVC(17)_gamma	"scale"
sklearn.svm._classes.SVC(17)_kernel	"rbf"
sklearn.svm._classes.SVC(17)_max_iter	-1
sklearn.svm._classes.SVC(17)_probability	false
sklearn.svm._classes.SVC(17)_random_state	42
sklearn.svm._classes.SVC(17)_shrinking	true
sklearn.svm._classes.SVC(17)_tol	0.001
sklearn.svm._classes.SVC(17)_verbose	false
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler),classifier=sklearn.svm._classes.SVC)(1)_memory	null
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler),classifier=sklearn.svm._classes.SVC)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "preprocessing", "step_name": "preprocessing"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler),classifier=sklearn.svm._classes.SVC)(1)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler)(1)_force_int_remainder_cols	true
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler)(1)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler)(1)_remainder	"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler)(1)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler)(1)_transformer_weights	null
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler)(1)_transformers	[{"oml-python:serialized_object": "component_reference", "value": {"key": "num", "step_name": "num", "argument_1": ["V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", "V39", "V40", "V41", "V42", "V43", "V44", "V45", "V46", "V47", "V48", "V49", "V50", "V51", "V52", "V53", "V54", "V55", "V56", "V57", "V58", "V59", "V60", "V61", "V62", "V63", "V64", "V65", "V66", "V67", "V68", "V69", "V70", "V71", "V72"]}}]
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler)(1)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler)(1)_verbose_feature_names_out	true