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mlr.regr.rpart (1)
Learner mlr.regr.rpart from package(s) rpart.
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mlr.classif.randomForest.preproc (1)
Learner mlr.classif.randomForest.preproc from package(s) randomForest.
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classif.bartMachine (6)
Learner classif.bartMachine from package(s) bartMachine.
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mlr.classif.randomForest.tuned (7)
Learner mlr.classif.randomForest.tuned from package(s) randomForest.
2 runs0 likes1 downloads1 reach0 impact
classif.logreg (12)
Learner classif.logreg from package(s) stats.
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classif.randomForest (58)
Learner classif.randomForest from package(s) randomForest.
2 runs0 likes1 downloads1 reach0 impact
mlr.classif.J48 (1)
Learner mlr.classif.J48 from package(s) RWeka.
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weka.MultiSearch_IBk (1)
Weka implementation of MultiSearch
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weka.FilteredClassifier_MultiSearch_J48 (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_MultiSearch_AdditiveRegression_REPTree (1)
Weka implementation of FilteredClassifier
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mlr.regr.cforest (1)
Learner mlr.regr.cforest from package(s) party.
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mlr.regr.ctree (1)
Learner mlr.regr.ctree from package(s) party.
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mlr.regr.earth (1)
Learner mlr.regr.earth from package(s) earth.
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mlr.regr.fnn (1)
Learner mlr.regr.fnn from package(s) FNN.
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mlr.regr.gbm (1)
Learner mlr.regr.gbm from package(s) gbm.
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mlr.regr.glmnet (1)
Learner mlr.regr.glmnet from package(s) glmnet.
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mlr.regr.ksvm (1)
Learner mlr.regr.ksvm from package(s) kernlab.
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mlr.regr.lm (1)
Learner mlr.regr.lm from package(s) stats.
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mlr.regr.nnet (1)
Learner mlr.regr.nnet from package(s) nnet.
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mlr.regr.pcr (1)
Learner mlr.regr.pcr from package(s) pls.
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mlr.regr.penalized.lasso (1)
Learner mlr.regr.penalized.lasso from package(s) !penalized.
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mlr.regr.penalized.ridge (1)
Learner mlr.regr.penalized.ridge from package(s) !penalized.
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mlr.regr.plsr (1)
Learner mlr.regr.plsr from package(s) pls.
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mlr.regr.randomForest (1)
Learner mlr.regr.randomForest from package(s) randomForest.
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mlr.regr.rsm (1)
Learner mlr.regr.rsm from package(s) rsm.
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mlr.regr.rvm (1)
Learner mlr.regr.rvm from package(s) kernlab.
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mlr.classif.naiveBayes.preproc.preproc (1)
Learner mlr.classif.naiveBayes.preproc.preproc from package(s) e1071.
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mlr.classif.naiveBayes.preproc.preproc (3)
Learner mlr.classif.naiveBayes.preproc.preproc from package(s) e1071.
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mlr.classif.rpart.preproc.preproc (3)
Learner mlr.classif.rpart.preproc.preproc from package(s) rpart.
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mlr.classif.kknn.preproc.preproc (9)
Learner mlr.classif.kknn.preproc.preproc from package(s) !kknn.
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mlr.classif.mlp.preproc.preproc (3)
Learner mlr.classif.mlp.preproc.preproc from package(s) RSNNS.
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mlr.classif.logreg.preproc.preproc (1)
Learner mlr.classif.logreg.preproc.preproc from package(s) stats.
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mlr.classif.PART.preproc.preproc (3)
Learner mlr.classif.PART.preproc.preproc from package(s) RWeka.
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mlr.classif.randomForest.preproc.preproc (2)
Learner mlr.classif.randomForest.preproc.preproc from package(s) randomForest.
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mlr.classif.rpart (10)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.glmboost (2)
Learner mlr.classif.glmboost from package(s) mboost.
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mlr.regr.penalized.fusedlasso (1)
Learner mlr.regr.penalized.fusedlasso from package(s) !penalized.
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mlr.classif.rpart (13)
Learner mlr.classif.rpart from package(s) rpart.
2 runs0 likes0 downloads0 reach1 impact
sklearn.ensemble.forest.RandomForestClassifier (12)
Flow generated by run_task
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mlr.classif.rpart.tuned (2)
Learner mlr.classif.rpart.tuned from package(s) rpart.
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mlr.classif.rpart.preproc (17)
Learner mlr.classif.rpart.preproc from package(s) rpart.
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mlr.classif.rpart.bagged.preproc (2)
Learner mlr.classif.rpart.bagged.preproc from package(s) rpart.
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mlr.weightedclasses.classif.gbm.preproc (1)
Learner mlr.weightedclasses.classif.gbm.preproc from package(s) gbm.
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mlr.weightedclasses.classif.randomForestSRC (1)
Learner mlr.weightedclasses.classif.randomForestSRC from package(s) randomForestSRC.
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weka.CostSensitiveClassifier_LMT (1)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_HoeffdingTree (1)
Weka implementation of CostSensitiveClassifier
2 runs0 likes0 downloads0 reach1 impact
weka.Bagging_J48 (8)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.FilteredClassifier_CostSensitiveClassifier_RandomTree (2)
Weka implementation of FilteredClassifier
2 runs0 likes0 downloads0 reach0 impact
weka.LibSVM (9)
Yasser EL-Manzalawy (2005). WLSVM. URL http://www.cs.iastate.edu/~yasser/wlsvm/. Chih-Chung Chang, Chih-Jen Lin (2001). LIBSVM - A Library for Support Vector Machines. URL…
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weka.CostSensitiveClassifier_VotedPerceptron (1)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_LogitBoost_RandomForest (1)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_J48 (5)
Weka implementation of CostSensitiveClassifier
2 runs0 likes0 downloads0 reach1 impact
weka.CostSensitiveClassifier_Bagging_J48 (2)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_IterativeClassifierOptimizer_LogitBoost_DecisionStump (2)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_JRip (2)
Weka implementation of CostSensitiveClassifier
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weka.RandomCommittee_Bagging_LogitBoost_DecisionStump (1)
Weka implementation of RandomCommittee
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weka.CostSensitiveClassifier_Bagging_REPTree (4)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_DecisionTable (1)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_Bagging_SimpleLogistic (1)
Weka implementation of CostSensitiveClassifier
2 runs0 likes0 downloads0 reach1 impact
weka.CostSensitiveClassifier_NaiveBayes (3)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_RandomSubSpace_SimpleLogistic (1)
Weka implementation of CostSensitiveClassifier
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weka.RandomSubSpace_SimpleLogistic (1)
Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL…
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weka.CostSensitiveClassifier_Bagging_IBk (1)
Weka implementation of CostSensitiveClassifier
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weka.Vote_NaiveBayes (2)
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…
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weka.AdaBoostM1_NaiveBayes (4)
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.CostSensitiveClassifier_Bagging_LogitBoost_DecisionStump (3)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_Bagging_AdaBoostM1_DecisionStump (1)
Weka implementation of CostSensitiveClassifier
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__main__.Flow (1)
Flow generated by run_task
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mlr.classif.randomForest (22)
Learner mlr.classif.randomForest from package(s) randomForest.
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mlr.classif.rpart.tuned (5)
Learner mlr.classif.rpart.tuned from package(s) rpart.
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mlr.classif.randomForest (23)
Learner mlr.classif.randomForest from package(s) randomForest.
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sklearn.naive_bayes.GaussianNB (1)
Automatically created sub-component.
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mlr.classif.RRF.preproc (1)
Learner mlr.classif.RRF.preproc from package(s) RRF.
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mlr.classif.glmnet (2)
Learner mlr.classif.glmnet from package(s) glmnet.
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sklearn.pipeline.Pipeline(classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(feature_select=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.RandomForestClassifier),classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.RandomForestClassifier,voting=sklearn.ensemble.forest.ExtraTreesClassifier,weights=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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mlr.classif.rpart.tuned (6)
Learner mlr.classif.rpart.tuned from package(s) rpart.
2 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,selectfrommodel=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,selectfrommodel=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.ExtraTreesClassifier,voting=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
2 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,votingclassifier=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.
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.svm.classes.SVC)) (1)
Automatically created sub-component.
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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Feature_Removal=sklearn.feature_selection.univariate_selection.SelectKBest,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.weight_boosting.AdaBoostClassifier (5)
Automatically created sub-component.
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sklearn.ensemble.voting_classifier.VotingClassifier(voting=sklearn.ensemble.forest.RandomForestClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=__main__.SecondGradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
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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.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)) (1)
Automatically created scikit-learn flow.
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mlr.regr.rpart (4)
Learner mlr.regr.rpart from package(s) rpart.
2 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,kernelpca=sklearn.decomposition.kernel_pca.KernelPCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
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weka.MultiSearch_J48 (5)
Weka implementation of MultiSearch
2 runs0 likes0 downloads0 reach2 impact
weka.Bagging_MultilayerPerceptron (4)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
2 runs0 likes0 downloads0 reach0 impact
weka.Stacking_ZeroR (3)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
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weka.OneR (21)
R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
2 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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weka.Stacking_AttributeSelectedClassifier (1)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,robustscaler=sklearn.preprocessing.data.RobustScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
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weka.CostSensitiveClassifier_MultilayerPerceptron (2)
Weka implementation of CostSensitiveClassifier
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weka.FilteredClassifier_J48 (2)
Weka implementation of FilteredClassifier
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