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__main__.DI (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(LogisticRegression_1=sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression),LogisticRegression_2=sklearn.pipeline.Pipeline(di=__main__.DI,robustscaler=sklearn.preprocessing.data.RobustScaler,lr=__main__.LR)) (1)
Automatically created scikit-learn flow.
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weka.SMO_StringKernel (1)
J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning,…
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weka.StringKernel (2)
Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J. C. H. Watkins (2002). Text Classification using String Kernels. Journal of Machine Learning Research. 2:419-444. F.…
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weka.AdditiveRegression_DecisionStump (6)
J.H. Friedman (1999). Stochastic Gradient Boosting.
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weka.VotedPerceptron (6)
Y. Freund, R. E. Schapire: Large margin classification using the perceptron algorithm. In: 11th Annual Conference on Computational Learning Theory, New York, NY, 209-217, 1998.
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weka.MultiSearch_SimpleLogistic (1)
Weka implementation of MultiSearch
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lightgbm.sklearn.LGBMClassifier (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(RFC=sklearn.ensemble.forest.RandomForestClassifier,ETC=sklearn.ensemble.forest.ExtraTreesClassifier)) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(RFC=sklearn.ensemble.forest.RandomForestClassifier,ETC=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(RandomForestClassifier=sklearn.ensemble.forest.RandomForestClassifier,ExtraTreesClassifier=sklearn.ensemble.forest.ExtraTreesClassifier)) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(RandomForestClassifier=sklearn.ensemble.forest.RandomForestClassifier,ExtraTreesClassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,voter=helper.voter.Voter) (1)
Automatically created scikit-learn flow.
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helper.voter.Voter (1)
Automatically created scikit-learn flow.
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mlr.classif.rpart (33)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.rpart (33)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.rpart (33)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.rpart (33)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.rpart (33)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.rpart (34)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.rpart (35)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.rpart (34)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.rpart (35)
Learner mlr.classif.rpart from package(s) rpart.
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weka.CostSensitiveClassifier_FilteredClassifier_Bagging_RandomForest (1)
Weka implementation of CostSensitiveClassifier
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sklearn.kernel_approximation.Nystroem (1)
Automatically created scikit-learn flow.
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weka.FilteredClassifier_CostSensitiveClassifier_IBk (1)
Weka implementation of FilteredClassifier
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weka.CostSensitiveClassifier_IBk (2)
Weka implementation of CostSensitiveClassifier
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sklearn.pipeline.Pipeline(DualImputer=helper.dual_imputer.DualImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,standardscaler=sklearn.preprocessing.data.StandardScaler,linearsvc=sklearn.svm.classes.LinearSVC) (1)
Automatically created scikit-learn flow.
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weka.FilteredClassifier_FilteredClassifier_CostSensitiveClassifier_MultiSearch_SMO_PolyKernel (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_CostSensitiveClassifier_MultiSearch_SMO_PolyKernel (1)
Weka implementation of FilteredClassifier
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weka.CostSensitiveClassifier_MultiSearch_SMO_PolyKernel (1)
Weka implementation of CostSensitiveClassifier
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weka.MultiSearch_SMO_PolyKernel (1)
Weka implementation of MultiSearch
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weka.FilteredClassifier_FilteredClassifier_CostSensitiveClassifier_GaussianProcesses_PolyKernel (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_CostSensitiveClassifier_GaussianProcesses_PolyKernel (1)
Weka implementation of FilteredClassifier
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weka.CostSensitiveClassifier_GaussianProcesses_PolyKernel (3)
Weka implementation of CostSensitiveClassifier
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weka.FilteredClassifier_FilteredClassifier_CostSensitiveClassifier_MultilayerPerceptron (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_CostSensitiveClassifier_MultilayerPerceptron (1)
Weka implementation of FilteredClassifier
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weka.Stacking_CostSensitiveClassifier (1)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
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weka.CostSensitiveClassifier (3)
Weka implementation of CostSensitiveClassifier
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weka.FilteredClassifier_CostSensitiveClassifier_AdaBoostM1_DecisionStump (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_FilteredClassifier_Bagging_IBk (1)
Weka implementation of FilteredClassifier
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weka.Bagging_IBk (6)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.FilteredClassifier_FilteredClassifier_Bagging_SMO_PolyKernel (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_Bagging_SMO_PolyKernel (1)
Weka implementation of FilteredClassifier
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weka.Bagging_SMO_PolyKernel (3)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.FilteredClassifier_FilteredClassifier_FilteredClassifier_Bagging_MultilayerPerceptron (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_FilteredClassifier_Bagging_MultilayerPerceptron (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_Bagging_MultilayerPerceptron (1)
Weka implementation of FilteredClassifier
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weka.ClassificationViaRegression_SMO_PolyKernel (1)
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
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weka.FilteredClassifier_FilteredClassifier_J48 (1)
Weka implementation of FilteredClassifier
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weka.CostSensitiveClassifier_Bagging_RandomForest (4)
Weka implementation of CostSensitiveClassifier
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weka.NaiveBayesMultinomialUpdateable (9)
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
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weka.REPTree (19)
Weka implementation of REPTree
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weka.CostSensitiveClassifier_AttributeSelectedClassifier_AdaBoostM1_Logistic (1)
Weka implementation of CostSensitiveClassifier
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weka.AttributeSelectedClassifier_AdaBoostM1_Logistic (1)
Weka implementation of AttributeSelectedClassifier
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weka.RandomizableFilteredClassifier (1)
Weka implementation of RandomizableFilteredClassifier
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weka.CostSensitiveClassifier_AdaBoostM1_SMO_PolyKernel (1)
Weka implementation of CostSensitiveClassifier
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weka.AdaBoostM1_SMO_PolyKernel (3)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
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weka.SMO_PolyKernel (16)
J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning,…
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weka.PolyKernel (12)
Weka implementation of PolyKernel
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weka.Logistic (9)
le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.
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weka.FilteredClassifier_Bagging_Logistic (1)
Weka implementation of FilteredClassifier
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weka.Bagging_Logistic (2)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.AdaBoostM1_AttributeSelectedClassifier_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.
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weka.AttributeSelectedClassifier_CostSensitiveClassifier_RandomForest (1)
Weka implementation of AttributeSelectedClassifier
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sklearn.neural_network.multilayer_perceptron.MLPClassifier (14)
Automatically created scikit-learn flow.
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mlr.classif.randomForest (42)
Learner mlr.classif.randomForest from package(s) randomForest.
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sklearn.preprocessing.imputation.Imputer (25)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.OneHotEncoder (25)
Automatically created scikit-learn flow.
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sklearn.ensemble.forest.RandomForestClassifier (39)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,kerasclassifier=keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,Conv2D,MaxPooling2D,Flatten,Dense,Activation,Dense,Activation)) (1)
Automatically created scikit-learn flow.
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keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,Conv2D,MaxPooling2D,Flatten,Dense,Activation,Dense,Activation) (4)
Automatically created scikit-learn flow.
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__main__.RandomForestWithPCA (1)
Automatically created scikit-learn flow.
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__main__.GradientBoostingWithPCA (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)) (3)
Automatically created scikit-learn flow.
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openmlstudy14.preprocessing.ConditionalImputer (8)
Automatically created scikit-learn flow.
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sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier) (5)
Automatically created scikit-learn flow.
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sklearn.tree.tree.DecisionTreeClassifier (24)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))) (3)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier) (6)
Automatically created scikit-learn flow.
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sklearn.ensemble.forest.RandomForestClassifier (40)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)) (4)
Automatically created scikit-learn flow.
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__main__.NNAugmentedInput (1)
Automatically created scikit-learn flow.
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openmlstudy99.preprocessing.ConditionalImputer (1)
Automatically created scikit-learn flow.
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openmldefaults.search.default_search.DefaultSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))) (1)
Automatically created scikit-learn flow.
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sklearn.preprocessing.imputation.Imputer (26)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.OneHotEncoder (26)
Automatically created scikit-learn flow.
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openmldefaults.search.default_search.DefaultSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)) (1)
Automatically created scikit-learn flow.
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openmldefaults.search.default_search.DefaultSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC) (3)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.StandardScaler (10)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)) (2)
Automatically created scikit-learn flow.
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weka.classifiers.meta.multisearch.RandomSearch (1)
Weka implementation.
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weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.RBFKernel,weka.classifiers.functions.Logistic)) (1)
Weka implementation.
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weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize) (1)
Weka implementation.
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weka.filters.unsupervised.attribute.ReplaceMissingValues (1)
Weka implementation.
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weka.filters.unsupervised.attribute.RemoveUseless (1)
Weka implementation.
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weka.filters.unsupervised.attribute.Normalize (1)
Weka implementation.
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weka.classifiers.functions.supportVector.RBFKernel (1)
Weka implementation.
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weka.classifiers.functions.Logistic (1)
le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.
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