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Task 10101 (Supervised Classification) blood-transfusion-service-center Uploaded 14-08-2021 by Sergey Redyuk
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sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,gradientboostin gclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)( 2)	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 to None.
sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(2)_memory	null
sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(2)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "pca", "step_name": "pca"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}]
sklearn.decomposition.pca.PCA(12)_copy	true
sklearn.decomposition.pca.PCA(12)_iterated_power	9
sklearn.decomposition.pca.PCA(12)_n_components	null
sklearn.decomposition.pca.PCA(12)_random_state	11584
sklearn.decomposition.pca.PCA(12)_svd_solver	"randomized"
sklearn.decomposition.pca.PCA(12)_tol	0.0
sklearn.decomposition.pca.PCA(12)_whiten	false
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_criterion	"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_init	null
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_learning_rate	0.5
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_loss	"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_max_depth	4
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_max_features	0.6000000000000001
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_max_leaf_nodes	null
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_min_impurity_decrease	0.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_min_impurity_split	null
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_min_samples_leaf	8
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_min_samples_split	11
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_min_weight_fraction_leaf	0.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_n_estimators	100
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_presort	"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_random_state	18536
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_subsample	0.15000000000000002
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_verbose	0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(27)_warm_start	false