Run 8886125

Task 167132 (Supervised Classification) Speech Uploaded 16-03-2018 by P Bos
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Flow

sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer, scaler=sklearn.preprocessing.data.StandardScaler,dimension_reduction=sklear n.decomposition.pca.PCA,Classifier=sklearn.ensemble.voting_classifier.Votin gClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,rf=sk learn.ensemble.forest.RandomForestClassifier))(1)	Automatically created scikit-learn flow.
sklearn.preprocessing.data.StandardScaler(5)_copy	true
sklearn.preprocessing.data.StandardScaler(5)_with_mean	true
sklearn.preprocessing.data.StandardScaler(5)_with_std	true
sklearn.neighbors.classification.KNeighborsClassifier(20)_algorithm	"auto"
sklearn.neighbors.classification.KNeighborsClassifier(20)_leaf_size	30
sklearn.neighbors.classification.KNeighborsClassifier(20)_metric	"minkowski"
sklearn.neighbors.classification.KNeighborsClassifier(20)_metric_params	null
sklearn.neighbors.classification.KNeighborsClassifier(20)_n_jobs	1
sklearn.neighbors.classification.KNeighborsClassifier(20)_n_neighbors	8
sklearn.neighbors.classification.KNeighborsClassifier(20)_p	2
sklearn.neighbors.classification.KNeighborsClassifier(20)_weights	"uniform"
sklearn.preprocessing.imputation.Imputer(15)_axis	0
sklearn.preprocessing.imputation.Imputer(15)_copy	true
sklearn.preprocessing.imputation.Imputer(15)_missing_values	"NaN"
sklearn.preprocessing.imputation.Imputer(15)_strategy	"mean"
sklearn.preprocessing.imputation.Imputer(15)_verbose	0
sklearn.ensemble.forest.RandomForestClassifier(32)_bootstrap	true
sklearn.ensemble.forest.RandomForestClassifier(32)_class_weight	null
sklearn.ensemble.forest.RandomForestClassifier(32)_criterion	"gini"
sklearn.ensemble.forest.RandomForestClassifier(32)_max_depth	null
sklearn.ensemble.forest.RandomForestClassifier(32)_max_features	"auto"
sklearn.ensemble.forest.RandomForestClassifier(32)_max_leaf_nodes	null
sklearn.ensemble.forest.RandomForestClassifier(32)_min_impurity_decrease	0.0
sklearn.ensemble.forest.RandomForestClassifier(32)_min_impurity_split	null
sklearn.ensemble.forest.RandomForestClassifier(32)_min_samples_leaf	1
sklearn.ensemble.forest.RandomForestClassifier(32)_min_samples_split	2
sklearn.ensemble.forest.RandomForestClassifier(32)_min_weight_fraction_leaf	0.0
sklearn.ensemble.forest.RandomForestClassifier(32)_n_estimators	30
sklearn.ensemble.forest.RandomForestClassifier(32)_n_jobs	1
sklearn.ensemble.forest.RandomForestClassifier(32)_oob_score	false
sklearn.ensemble.forest.RandomForestClassifier(32)_random_state	1
sklearn.ensemble.forest.RandomForestClassifier(32)_verbose	0
sklearn.ensemble.forest.RandomForestClassifier(32)_warm_start	false
sklearn.decomposition.pca.PCA(2)_copy	true
sklearn.decomposition.pca.PCA(2)_iterated_power	"auto"
sklearn.decomposition.pca.PCA(2)_n_components	null
sklearn.decomposition.pca.PCA(2)_random_state	15660
sklearn.decomposition.pca.PCA(2)_svd_solver	"auto"
sklearn.decomposition.pca.PCA(2)_tol	0.0
sklearn.decomposition.pca.PCA(2)_whiten	false
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,scaler=sklearn.preprocessing.data.StandardScaler,dimension_reduction=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,rf=sklearn.ensemble.forest.RandomForestClassifier))(1)_memory	null
sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,rf=sklearn.ensemble.forest.RandomForestClassifier)(1)_flatten_transform	null
sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,rf=sklearn.ensemble.forest.RandomForestClassifier)(1)_n_jobs	1
sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,rf=sklearn.ensemble.forest.RandomForestClassifier)(1)_voting	"soft"
sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,rf=sklearn.ensemble.forest.RandomForestClassifier)(1)_weights	[9, 1]