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Task 9980 (Supervised Classification) climate-model-simulation-crashes Uploaded 03-09-2021 by Victorien Fandos
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sklearn.pipeline.Pipeline(MinMaxScaler=sklearn.preprocessing._data.MinMaxSc aler,svc=sklearn.svm._classes.SVC)(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``.
sklearn.svm._classes.SVC(9)_C	2
sklearn.svm._classes.SVC(9)_break_ties	false
sklearn.svm._classes.SVC(9)_cache_size	200
sklearn.svm._classes.SVC(9)_class_weight	null
sklearn.svm._classes.SVC(9)_coef0	0.0
sklearn.svm._classes.SVC(9)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(9)_degree	3
sklearn.svm._classes.SVC(9)_gamma	0.2222223
sklearn.svm._classes.SVC(9)_kernel	"poly"
sklearn.svm._classes.SVC(9)_max_iter	-1
sklearn.svm._classes.SVC(9)_probability	false
sklearn.svm._classes.SVC(9)_random_state	14563
sklearn.svm._classes.SVC(9)_shrinking	true
sklearn.svm._classes.SVC(9)_tol	0.001
sklearn.svm._classes.SVC(9)_verbose	false
sklearn.pipeline.Pipeline(MinMaxScaler=sklearn.preprocessing._data.MinMaxScaler,svc=sklearn.svm._classes.SVC)(1)_memory	null
sklearn.pipeline.Pipeline(MinMaxScaler=sklearn.preprocessing._data.MinMaxScaler,svc=sklearn.svm._classes.SVC)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "MinMaxScaler", "step_name": "MinMaxScaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}]
sklearn.pipeline.Pipeline(MinMaxScaler=sklearn.preprocessing._data.MinMaxScaler,svc=sklearn.svm._classes.SVC)(1)_verbose	false
sklearn.preprocessing._data.MinMaxScaler(3)_clip	false
sklearn.preprocessing._data.MinMaxScaler(3)_copy	true
sklearn.preprocessing._data.MinMaxScaler(3)_feature_range	[0, 1]