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Task 10101 (Supervised Classification) blood-transfusion-service-center Uploaded 13-08-2021 by Sergey Redyuk
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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer, polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklear n.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.Rand omForestClassifier)(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 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.preprocessing.data.PolynomialFeatures(6)_degree	2
sklearn.preprocessing.data.PolynomialFeatures(6)_include_bias	true
sklearn.preprocessing.data.PolynomialFeatures(6)_interaction_only	false
sklearn.decomposition.pca.PCA(11)_copy	true
sklearn.decomposition.pca.PCA(11)_iterated_power	"auto"
sklearn.decomposition.pca.PCA(11)_n_components	null
sklearn.decomposition.pca.PCA(11)_random_state	479
sklearn.decomposition.pca.PCA(11)_svd_solver	"auto"
sklearn.decomposition.pca.PCA(11)_tol	0.0
sklearn.decomposition.pca.PCA(11)_whiten	false
sklearn.ensemble.forest.RandomForestClassifier(67)_bootstrap	true
sklearn.ensemble.forest.RandomForestClassifier(67)_class_weight	null
sklearn.ensemble.forest.RandomForestClassifier(67)_criterion	"gini"
sklearn.ensemble.forest.RandomForestClassifier(67)_max_depth	null
sklearn.ensemble.forest.RandomForestClassifier(67)_max_features	"auto"
sklearn.ensemble.forest.RandomForestClassifier(67)_max_leaf_nodes	null
sklearn.ensemble.forest.RandomForestClassifier(67)_min_impurity_split	1e-07
sklearn.ensemble.forest.RandomForestClassifier(67)_min_samples_leaf	1
sklearn.ensemble.forest.RandomForestClassifier(67)_min_samples_split	2
sklearn.ensemble.forest.RandomForestClassifier(67)_min_weight_fraction_leaf	0.0
sklearn.ensemble.forest.RandomForestClassifier(67)_n_estimators	10
sklearn.ensemble.forest.RandomForestClassifier(67)_n_jobs	1
sklearn.ensemble.forest.RandomForestClassifier(67)_oob_score	false
sklearn.ensemble.forest.RandomForestClassifier(67)_random_state	49568
sklearn.ensemble.forest.RandomForestClassifier(67)_verbose	0
sklearn.ensemble.forest.RandomForestClassifier(67)_warm_start	false
sklearn.preprocessing.imputation.Imputer(52)_axis	0
sklearn.preprocessing.imputation.Imputer(52)_copy	true
sklearn.preprocessing.imputation.Imputer(52)_missing_values	"NaN"
sklearn.preprocessing.imputation.Imputer(52)_strategy	"median"
sklearn.preprocessing.imputation.Imputer(52)_verbose	0
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "polynomialfeatures", "step_name": "polynomialfeatures"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "pca", "step_name": "pca"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]