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weka.FilteredClassifier_AdaBoostM1_MultilayerPerceptron (1)
Weka implementation of FilteredClassifier
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weka.AdaBoostM1_MultilayerPerceptron (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.FilteredClassifier_VotedPerceptron (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_SMOreg_PolyKernel (1)
Weka implementation of FilteredClassifier
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weka.SMOreg_PolyKernel (1)
S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, K.R.K. Murthy: Improvements to the SMO Algorithm for SVM Regression. In: IEEE Transactions on Neural Networks, 1999. A.J. Smola, B. Schoelkopf (1998). A…
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weka.MultiSearch_FilteredClassifier_SMO_RBFKernel (1)
Weka implementation of MultiSearch
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weka.AdaBoostM1_NaiveBayes (5)
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.FilteredClassifier_Vote_RandomForest (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_AdaBoostM1_SMO_PolyKernel (1)
Weka implementation of FilteredClassifier
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weka.Bagging_IterativeClassifierOptimizer_AdaBoostM1_J48 (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.IterativeClassifierOptimizer_AdaBoostM1_J48 (1)
Weka implementation of IterativeClassifierOptimizer
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weka.Vote_SimpleLogistic (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.MultiSearch_Bagging_J48 (1)
Weka implementation of MultiSearch
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weka.Bagging_AdaBoostM1_FilteredClassifier_J48 (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.Vote_SMO (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…
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weka.Vote_BayesNet (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…
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weka.Vote_IBk (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…
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weka.Vote_Bagging (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.Vote_RandomizableFilteredClassifier_RandomTree (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…
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weka.CostSensitiveClassifier_AdaBoostM1_OneR (1)
Weka implementation of CostSensitiveClassifier
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weka.AdaBoostM1_OneR (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.FilteredClassifier_CostSensitiveClassifier_AdaBoostM1_MultiSearch_LinearRegression (1)
Weka implementation of FilteredClassifier
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weka.CostSensitiveClassifier_AdaBoostM1_MultiSearch_LinearRegression (1)
Weka implementation of CostSensitiveClassifier
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weka.AdaBoostM1_MultiSearch_LinearRegression (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.MultiSearch_LinearRegression (2)
Weka implementation of MultiSearch
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weka.FilteredClassifier_MultiSearch_LinearRegression (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_CostSensitiveClassifier_AdaBoostM1_M5P (1)
Weka implementation of FilteredClassifier
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weka.CostSensitiveClassifier_AdaBoostM1_M5P (1)
Weka implementation of CostSensitiveClassifier
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weka.AdaBoostM1_M5P (2)
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.ExtraTree (2)
Pierre Geurts, Damien Ernst, Louis Wehenkel (2006). Extremely randomized trees. Machine Learning. 63(1):3-42.
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weka.RandomizableFilteredClassifier_RandomizableFilteredClassifier_BayesNet (1)
Weka implementation of RandomizableFilteredClassifier
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weka.FilteredClassifier_AdaBoostM1_PART (1)
Weka implementation of FilteredClassifier
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weka.MultiClassClassifier_J48 (2)
Weka implementation of MultiClassClassifier
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weka.Bagging_RandomizableFilteredClassifier_Bagging_MultiClassClassifier_J48 (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.RandomizableFilteredClassifier_Bagging_MultiClassClassifier_J48 (1)
Weka implementation of RandomizableFilteredClassifier
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weka.Vote_J48 (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.Vote_LMT (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…
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weka.LMT (7)
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Machine Learning. 95(1-2):161-205. Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European…
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weka.Bagging_RandomForest (13)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.Vote_RandomizableFilteredClassifier (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…
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weka.MultiBoostAB_DecisionStump (5)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
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weka.RandomizableFilteredClassifier (2)
Weka implementation of RandomizableFilteredClassifier
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weka.CostSensitiveClassifier_M5Rules (1)
Weka implementation of CostSensitiveClassifier
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weka.FilteredClassifier_FilteredClassifier_CostSensitiveClassifier_SimpleLogistic (1)
Weka implementation of FilteredClassifier
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weka.Bagging_DecisionStump (6)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.CVParameterSelection_Logistic (1)
R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
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weka.SMO (3)
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.CVParameterSelection_SimpleLogistic (1)
R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
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weka.RotationForest_CostSensitiveClassifier_RandomForest (1)
Juan J. Rodriguez, Ludmila I. Kuncheva, Carlos J. Alonso (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10):1619-1630.…
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weka.Bagging_Vote_RandomTree (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.CostSensitiveClassifier_RandomizableFilteredClassifier_SimpleLogistic (1)
Weka implementation of CostSensitiveClassifier
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weka.Stacking_ZeroR (4)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
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weka.FilteredClassifier_CostSensitiveClassifier_FilteredClassifier_AdaBoostM1_REPTree (1)
Weka implementation of FilteredClassifier
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weka.CostSensitiveClassifier_FilteredClassifier_AdaBoostM1_REPTree (1)
Weka implementation of CostSensitiveClassifier
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weka.FilteredClassifier_CostSensitiveClassifier_Bagging_REPTree (1)
Weka implementation of FilteredClassifier
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weka.AdaBoostM1_AdaBoostM1_DecisionStump (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.Stacking_Bagging (2)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
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weka.KStar (6)
John G. Cleary, Leonard E. Trigg: K*: An Instance-based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114, 1995.
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weka.CostSensitiveClassifier_AdditiveRegression_DecisionStump (1)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_SimpleLinearRegression (1)
Weka implementation of CostSensitiveClassifier
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weka.FilteredClassifier_CostSensitiveClassifier_IBk (2)
Weka implementation of FilteredClassifier
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weka.CostSensitiveClassifier_IBk (3)
Weka implementation of CostSensitiveClassifier
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weka.MetaCost_DecisionStump (1)
Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999.
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weka.FilteredClassifier_MetaCost_RandomForest (1)
Weka implementation of FilteredClassifier
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weka.MetaCost_RandomForest (1)
Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999.
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weka.Vote_RandomizableFilteredClassifier_RandomTree (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.RandomizableFilteredClassifier_RandomTree (2)
Weka implementation of RandomizableFilteredClassifier
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weka.FilteredClassifier_DecisionTable (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_FilteredClassifier (1)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_RandomizableFilteredClassifier_RandomForest (1)
Weka implementation of FilteredClassifier
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weka.RandomizableFilteredClassifier_Logistic (1)
Weka implementation of RandomizableFilteredClassifier
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weka.CostSensitiveClassifier_ClassificationViaRegression_SimpleLogistic (1)
Weka implementation of CostSensitiveClassifier
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weka.ClassificationViaRegression_SimpleLogistic (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.AdaBoostM1_SimpleLogistic (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.Vote_RandomizableFilteredClassifier_MultilayerPerceptron (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…
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weka.MultiClassClassifier_REPTree (2)
Weka implementation of MultiClassClassifier
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weka.AdaBoostM1_CostSensitiveClassifier_ZeroR (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.AdaBoostM1_CostSensitiveClassifier_REPTree (2)
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.AdaBoostM1_CostSensitiveClassifier_HoeffdingTree (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.CostSensitiveClassifier_Bagging_J48 (3)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_SimpleLinearRegression (2)
Weka implementation of CostSensitiveClassifier
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weka.SimpleLinearRegression (7)
Weka implementation of SimpleLinearRegression
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weka.CostSensitiveClassifier_M5Rules (2)
Weka implementation of CostSensitiveClassifier
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weka.M5Rules (6)
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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weka.CostSensitiveClassifier_LMT (4)
Weka implementation of CostSensitiveClassifier
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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,scaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,dec_tree=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier)) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.FeatureUnion(functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures) (1)
Automatically created scikit-learn flow.
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sklearn.preprocessing._function_transformer.FunctionTransformer (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.pipeline.Pipeline(robustscaler=sklearn.preprocessing.data.RobustScaler,extratreeclassifier=sklearn.tree.tree.ExtraTreeClassifier)) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(robustscaler=sklearn.preprocessing.data.RobustScaler,extratreeclassifier=sklearn.tree.tree.ExtraTreeClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,featureunion=sklearn.pipeline.FeatureUnion(functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures),votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.pipeline.Pipeline(robustscaler=sklearn.preprocessing.data.RobustScaler,extratreeclassifier=sklearn.tree.tree.ExtraTreeClassifier),KNN=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier),Dummy=sklearn.pipeline.Pipeline(dummyclassifier=sklearn.dummy.DummyClassifier))) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.pipeline.Pipeline(robustscaler=sklearn.preprocessing.data.RobustScaler,extratreeclassifier=sklearn.tree.tree.ExtraTreeClassifier),KNN=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier),Dummy=sklearn.pipeline.Pipeline(dummyclassifier=sklearn.dummy.DummyClassifier)) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(dummyclassifier=sklearn.dummy.DummyClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.dummy.DummyClassifier (1)
Automatically created scikit-learn flow.
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