Flow
ID's
Sort: most runs
22302 results
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,selectfrommodel=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.ExtraTreesClassifier,voting=sklearn.ensemble.forest.RandomForestClassifier,n_jobs=xgboost.sklearn.XGBClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.ExtraTreesClassifier,weights=sklearn.ensemble.forest.RandomForestClassifier,n_jobs=xgboost.sklearn.XGBClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.ExtraTreesClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=xgboost.sklearn.XGBClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
xgboost.sklearn.XGBClassifier (4)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created sub-component.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Feature_Removal=sklearn.feature_selection.univariate_selection.SelectKBest,Classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Feature_Removal=sklearn.feature_selection.univariate_selection.SelectKBest,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created sub-component.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(voting=sklearn.ensemble.forest.RandomForestClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=__main__.AUCBooster)) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created sub-component.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Feature_Removal=sklearn.feature_selection.univariate_selection.SelectKBest,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Feature_Removal=sklearn.feature_selection.univariate_selection.SelectKBest,Classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.gaussian_process.gpc.GaussianProcessClassifier (1)
Automatically created sub-component.
1 runs0 likes0 downloads0 reach0 impact
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created sub-component.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Imputer=openml.utils.preprocessing.ConditionalImputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.svm.classes.SVC)) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model.base.LinearRegression (2)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(pt1=helper.partial.PT1(transformer=helper.partial.IMP1),pt2=helper.partial.PT2(transformer=helper.partial.IMP2),pt3=helper.partial.PT3(transformer=sklearn.preprocessing.data.StandardScaler),onehotencoder=sklearn.preprocessing.data.OneHotEncoder,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
weka.Bagging_SimpleLogistic (2)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
1 runs0 likes0 downloads0 reach0 impact
weka.Bagging_JRip (5)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
1 runs0 likes0 downloads0 reach0 impact
weka.ClassificationViaRegression_M5P (5)
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
1 runs0 likes0 downloads0 reach0 impact
weka.AutoWEKAClassifier (2)
Weka implementation of AutoWEKAClassifier
1 runs1 likes1 downloads2 reach1 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
weka.Bagging_Stacking_ZeroR (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
1 runs0 likes0 downloads0 reach0 impact
weka.Bagging_SMO_RBFKernel (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
1 runs0 likes0 downloads0 reach0 impact
weka.Stacking_SMO (1)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
1 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_Vote_NaiveBayes (1)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,standardscaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
weka.MultiSearch_RandomForest (3)
Weka implementation of MultiSearch
1 runs0 likes0 downloads0 reach0 impact
weka.MultiSearch_Logistic (2)
Weka implementation of MultiSearch
1 runs0 likes0 downloads0 reach0 impact
weka.InputMappedClassifier_ZeroR (3)
Weka implementation of InputMappedClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_AttributeSelectedClassifier_NaiveBayes (1)
Weka implementation of AttributeSelectedClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.MultiSearch_ZeroR (1)
Weka implementation of MultiSearch
1 runs0 likes0 downloads0 reach0 impact
weka.MultiSearch_Stacking_ZeroR (1)
Weka implementation of MultiSearch
1 runs0 likes0 downloads0 reach0 impact
weka.MultiClassClassifierUpdateable_SGD (5)
Weka implementation of MultiClassClassifierUpdateable
1 runs0 likes0 downloads0 reach0 impact
weka.SGD (6)
Weka implementation of SGD
1 runs0 likes0 downloads0 reach1 impact
weka.CostSensitiveClassifier_VotedPerceptron (2)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.MultiSearch_CostSensitiveClassifier_SMO_PolyKernel (1)
Weka implementation of MultiSearch
1 runs0 likes0 downloads0 reach0 impact
weka.MultiSearch_CostSensitiveClassifier_SimpleLogistic (1)
Weka implementation of MultiSearch
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,multinomialnb=sklearn.naive_bayes.MultinomialNB) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.svm.classes.NuSVC (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,pca=sklearn.decomposition.pca.PCA,nusvc=sklearn.svm.classes.NuSVC) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,pca=sklearn.decomposition.pca.PCA,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,nusvc=sklearn.svm.classes.NuSVC) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,nusvc=sklearn.svm.classes.NuSVC) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,lgbmclassifier=lightgbm.sklearn.LGBMClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,gaussiannb=sklearn.naive_bayes.GaussianNB) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
__main__.Wrapper (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_AttributeSelectedClassifier_IBk (1)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.AdaBoostM1_CostSensitiveClassifier_RandomForest (1)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
1 runs0 likes1 downloads1 reach0 impact
weka.FilteredClassifier_FilteredClassifier_CostSensitiveClassifier_Logistic (1)
Weka implementation of FilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_FilteredClassifier_CostSensitiveClassifier_SMO_PolyKernel (1)
Weka implementation of FilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_FilteredClassifier_Bagging_REPTree (1)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_FilteredClassifier_CostSensitiveClassifier_NaiveBayes (1)
Weka implementation of FilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_FilteredClassifier_CostSensitiveClassifier_IBk (1)
Weka implementation of FilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(DualImputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,linearsvc=sklearn.svm.classes.LinearSVC) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(DualImputer=helper.dual_imputer.DualImputer,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
weka.MultiScheme_AutoWEKAClassifier (1)
Weka implementation of MultiScheme
1 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_FilteredClassifier_CostSensitiveClassifier_AdaBoostM1_DecisionStump (1)
Weka implementation of FilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.AdaBoostM1_DecisionStump (17)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
1 runs0 likes0 downloads0 reach1 impact
weka.DecisionStump (9)
Weka implementation of DecisionStump
1 runs0 likes0 downloads0 reach0 impact
weka.DecisionTable (3)
Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
1 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_FilteredClassifier_FilteredClassifier_Bagging_SMO_PolyKernel (1)
Weka implementation of FilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.ZeroR (15)
Weka implementation of ZeroR
1 runs0 likes0 downloads0 reach0 impact
weka.NaiveBayesMultinomial (6)
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
1 runs0 likes0 downloads0 reach0 impact
weka.CVParameterSelection_J48 (1)
R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
1 runs0 likes0 downloads0 reach0 impact
weka.Vote_AttributeSelectedClassifier (1)
Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc.. J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE…
1 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_FilteredClassifier_FilteredClassifier_J48 (1)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_FilteredClassifier_FilteredClassifier_RandomForest (1)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_CostSensitiveClassifier_AdaBoostM1_RandomForest (1)
Weka implementation of FilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_FilteredClassifier_Bagging_J48 (1)
Weka implementation of AttributeSelectedClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.AdaBoostM1_CostSensitiveClassifier_Logistic (1)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
1 runs0 likes0 downloads0 reach0 impact
weka.AdaBoostM1_Logistic (1)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
1 runs0 likes0 downloads0 reach1 impact
weka.Stacking_RandomizableFilteredClassifier (1)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
1 runs0 likes0 downloads0 reach0 impact
weka.Stacking_IBk (2)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
1 runs0 likes0 downloads0 reach0 impact
weka.MultiClassClassifier_SMO_RBFKernel (1)
Weka implementation of MultiClassClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.AdaBoostM1_CostSensitiveClassifier_Logistic (2)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
1 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_Logistic (4)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach1 impact
weka.CostSensitiveClassifier_MultiBoostAB_RandomForest (1)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.MultiBoostAB_RandomForest (3)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
1 runs0 likes0 downloads0 reach1 impact
weka.CostSensitiveClassifier_FilteredClassifier_IBk (1)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (52)
Learner mlr.classif.rpart from package(s) rpart.
1 runs0 likes0 downloads0 reach0 impact
keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,Conv2D,MaxPooling2D,Activation,Flatten,Dense,Dense,Dense) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,kerasclassifier=keras.wrappers.scikit_learn.KerasClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.tree.tree.ExtraTreeClassifier (17)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach1 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
__main__.GradientBoostingClassifierBOHB (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.svm.classes.LinearSVC (8)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(1=sklearn.preprocessing.imputation.Imputer,3=sklearn.preprocessing.data.StandardScaler,5=sklearn.cluster.hierarchical.FeatureAgglomeration,6=sklearn.linear_model.stochastic_gradient.SGDClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(1=sklearn.preprocessing.imputation.Imputer,2=sklearn.preprocessing.data.OneHotEncoder,3.0=sklearn.preprocessing._function_transformer.FunctionTransformer,3=sklearn.preprocessing.data.StandardScaler,5=sklearn.cluster.hierarchical.FeatureAgglomeration,6=sklearn.linear_model.stochastic_gradient.SGDClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact