OpenML

Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992. Y. Wang, I. H. Witten: Induction of model trees for…

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weka.GaussianProcesses_PolyKernel (11)

David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.

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weka.RandomCommittee_M5P (1)

Weka implementation of RandomCommittee

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weka.NormalizedPolyKernel (7)

Weka implementation of NormalizedPolyKernel

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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)

Automatically created scikit-learn flow.

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weka.IsolationForest (4)

Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou: Isolation Forest. In: ICDM, 413-422, 2008.

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weka.LOF (1)

Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jorg Sander (2000). LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record. 29(2):93-104.

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weka.Bagging_Stacking_ZeroR (1)

Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.

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weka.Bagging_SMO_RBFKernel (1)

Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.

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weka.Stacking_SMO (1)

David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.

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weka.SMO (2)

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.OneR (21)

R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.

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mlr.classif.ranger (10)

Learner mlr.classif.ranger from package(s) ranger.

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mlr.classif.svm (8)

Learner mlr.classif.svm from package(s) e1071.

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mlr.classif.kknn (11)

Learner mlr.classif.kknn from package(s) !kknn.

2419 runs0 likes0 downloads0 reach0 impact

weka.CostSensitiveClassifier_LinearRegression (3)

Weka implementation of CostSensitiveClassifier

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weka.CostSensitiveClassifier_Vote_NaiveBayes (1)

Weka implementation of CostSensitiveClassifier

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weka.Vote_NaiveBayes (3)

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.HNB (8)

H. Zhang, L. Jiang, J. Su: Hidden Naive Bayes. In: Twentieth National Conference on Artificial Intelligence, 919-924, 2005.

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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (1)

Automatically created scikit-learn flow.

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helper.dual_imputer.DualImputer (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,neuralnetwork=helper.nn.NeuralNetwork) (1)

Automatically created scikit-learn flow.

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helper.nn.NeuralNetwork (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,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,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,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.

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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,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,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.

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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(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)

Automatically created scikit-learn flow.

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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.

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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.

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weka.M5Rules (5)

Geoffrey Holmes, Mark Hall, Eibe Frank: Generating Rule Sets from Model Trees. In: Twelfth Australian Joint Conference on Artificial Intelligence, 1-12, 1999. Ross J. Quinlan: Learning with Continuous…

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mlr.classif.glmnet (6)

Learner mlr.classif.glmnet from package(s) glmnet.

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weka.ClassificationViaRegression_J48 (2)

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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mlr.classif.xgboost (7)

Learner mlr.classif.xgboost from package(s) xgboost.

13577 runs0 likes1 downloads1 reach0 impact

weka.MultiSearch_RandomForest (3)

Weka implementation of MultiSearch

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weka.MultiSearch_Logistic (2)

Weka implementation of MultiSearch

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weka.InputMappedClassifier_ZeroR (3)

Weka implementation of InputMappedClassifier

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sklearn.pipeline.Pipeline(svc=sklearn.svm.classes.SVC) (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.AttributeSelectedClassifier_Stacking_SMO (1)

Weka implementation of AttributeSelectedClassifier

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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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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,robustscaler=sklearn.preprocessing.data.RobustScaler,lr=__main__.LR) (1)

Automatically created scikit-learn flow.

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__main__.LR (1)

Automatically created scikit-learn flow.

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sklearn.ensemble.voting_classifier.VotingClassifier(Standard=sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression),Robust=sklearn.pipeline.Pipeline(di=__main__.DI,robustscaler=sklearn.preprocessing.data.RobustScaler,lr=__main__.LR)) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(di=__main__.DI,robustscaler=sklearn.preprocessing.data.RobustScaler,lr=__main__.LR) (1)

Automatically created scikit-learn flow.

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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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sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,pca=sklearn.decomposition.pca.PCA,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (1)

Automatically created scikit-learn flow.

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weka.AttributeSelectedClassifier_AttributeSelectedClassifier_NaiveBayes (1)

Weka implementation of AttributeSelectedClassifier

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weka.MultiSearch_ZeroR (1)

Weka implementation of MultiSearch

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weka.MultiSearch_Stacking_ZeroR (1)

Weka implementation of MultiSearch

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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.MultiClassClassifierUpdateable_SGD (5)

Weka implementation of MultiClassClassifierUpdateable

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weka.CostSensitiveClassifier_VotedPerceptron (2)

Weka implementation of CostSensitiveClassifier

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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_CostSensitiveClassifier_SMO_PolyKernel (1)

Weka implementation of MultiSearch

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