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Task 9983 (Supervised Classification) eeg-eye-state Uploaded 21-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,step_1=skle arn.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(4)_C	0.00029315183841751896
sklearn.svm._classes.SVC(4)_break_ties	true
sklearn.svm._classes.SVC(4)_cache_size	200
sklearn.svm._classes.SVC(4)_class_weight	null
sklearn.svm._classes.SVC(4)_coef0	-13.83599139778536
sklearn.svm._classes.SVC(4)_decision_function_shape	"ovr"
sklearn.svm._classes.SVC(4)_degree	3
sklearn.svm._classes.SVC(4)_gamma	0.0038821774248374162
sklearn.svm._classes.SVC(4)_kernel	"sigmoid"
sklearn.svm._classes.SVC(4)_max_iter	-1
sklearn.svm._classes.SVC(4)_probability	false
sklearn.svm._classes.SVC(4)_random_state	42
sklearn.svm._classes.SVC(4)_shrinking	true
sklearn.svm._classes.SVC(4)_tol	0.10874189109172291
sklearn.svm._classes.SVC(4)_verbose	false
sklearn.impute._knn.KNNImputer(1)_add_indicator	true
sklearn.impute._knn.KNNImputer(1)_copy	false
sklearn.impute._knn.KNNImputer(1)_metric	"nan_euclidean"
sklearn.impute._knn.KNNImputer(1)_missing_values	NaN
sklearn.impute._knn.KNNImputer(1)_n_neighbors	38
sklearn.impute._knn.KNNImputer(1)_weights	"distance"
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,step_1=sklearn.svm._classes.SVC)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,step_1=sklearn.svm._classes.SVC)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,step_1=sklearn.svm._classes.SVC)(1)_verbose	false