Run 10560384

Task 9952 (Supervised Classification) phoneme Uploaded 13-08-2021 by Sergey Redyuk
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sklearn.pipeline.Pipeline(votingclassifier=sklearn.ensemble.voting_classifi er.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standa rdscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=s klearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.t ree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClas sifier,RFC=sklearn.ensemble.forest.RandomForestClassifier))(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.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	29084
sklearn.ensemble.forest.RandomForestClassifier(67)_verbose	0
sklearn.ensemble.forest.RandomForestClassifier(67)_warm_start	false
sklearn.preprocessing.data.StandardScaler(43)_copy	true
sklearn.preprocessing.data.StandardScaler(43)_with_mean	true
sklearn.preprocessing.data.StandardScaler(43)_with_std	true
sklearn.tree.tree.DecisionTreeClassifier(66)_class_weight	null
sklearn.tree.tree.DecisionTreeClassifier(66)_criterion	"gini"
sklearn.tree.tree.DecisionTreeClassifier(66)_max_depth	null
sklearn.tree.tree.DecisionTreeClassifier(66)_max_features	null
sklearn.tree.tree.DecisionTreeClassifier(66)_max_leaf_nodes	null
sklearn.tree.tree.DecisionTreeClassifier(66)_min_impurity_split	1e-07
sklearn.tree.tree.DecisionTreeClassifier(66)_min_samples_leaf	1
sklearn.tree.tree.DecisionTreeClassifier(66)_min_samples_split	2
sklearn.tree.tree.DecisionTreeClassifier(66)_min_weight_fraction_leaf	0.0
sklearn.tree.tree.DecisionTreeClassifier(66)_presort	false
sklearn.tree.tree.DecisionTreeClassifier(66)_random_state	54175
sklearn.tree.tree.DecisionTreeClassifier(66)_splitter	"best"
sklearn.tree.tree.ExtraTreeClassifier(28)_class_weight	null
sklearn.tree.tree.ExtraTreeClassifier(28)_criterion	"gini"
sklearn.tree.tree.ExtraTreeClassifier(28)_max_depth	1000
sklearn.tree.tree.ExtraTreeClassifier(28)_max_features	"auto"
sklearn.tree.tree.ExtraTreeClassifier(28)_max_leaf_nodes	null
sklearn.tree.tree.ExtraTreeClassifier(28)_min_impurity_split	1e-07
sklearn.tree.tree.ExtraTreeClassifier(28)_min_samples_leaf	1
sklearn.tree.tree.ExtraTreeClassifier(28)_min_samples_split	2
sklearn.tree.tree.ExtraTreeClassifier(28)_min_weight_fraction_leaf	0.0
sklearn.tree.tree.ExtraTreeClassifier(28)_random_state	54690
sklearn.tree.tree.ExtraTreeClassifier(28)_splitter	"random"
sklearn.pipeline.Pipeline(votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier))(2)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "votingclassifier", "step_name": "votingclassifier"}}]
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier)(2)_estimators	[{"oml-python:serialized_object": "component_reference", "value": {"key": "DecisionTreeClassifier", "step_name": "DecisionTreeClassifier"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "ExtraTreeClassifier", "step_name": "ExtraTreeClassifier"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "KNN", "step_name": "KNN"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "RFC", "step_name": "RFC"}}]
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier)(2)_n_jobs	1
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier)(2)_voting	"soft"
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier)(2)_weights	null
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(2)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
sklearn.neighbors.classification.KNeighborsClassifier(44)_algorithm	"auto"
sklearn.neighbors.classification.KNeighborsClassifier(44)_leaf_size	30
sklearn.neighbors.classification.KNeighborsClassifier(44)_metric	"minkowski"
sklearn.neighbors.classification.KNeighborsClassifier(44)_metric_params	null
sklearn.neighbors.classification.KNeighborsClassifier(44)_n_jobs	1
sklearn.neighbors.classification.KNeighborsClassifier(44)_n_neighbors	1
sklearn.neighbors.classification.KNeighborsClassifier(44)_p	2
sklearn.neighbors.classification.KNeighborsClassifier(44)_weights	"uniform"