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weka.MIWrapper (1)
E. T. Frank, X. Xu (2003). Applying propositional learning algorithms to multi-instance data. Department of Computer Science, University of Waikato, Hamilton, NZ.
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rm.operator.parallel_decision_tree (3)
A RapidMiner Operator
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rm.operator.logistic_regression (1)
A RapidMiner Operator
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rm.operator.support_vector_machine (2)
A RapidMiner Operator
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moa.Perceptron (3)
Moa implementation of Perceptron
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moa.Perceptron (4)
Moa implementation of Perceptron
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classif.naiveBayes (7)
Learner classif.naiveBayes from package(s) e1071.
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classif.J48 (23)
Learner classif.J48 from package(s) RWeka.
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classif.randomForest (52)
Learner classif.randomForest from package(s) randomForest.
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classif.randomForest (53)
Learner classif.randomForest from package(s) randomForest.
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classif.rpart (46)
Learner classif.rpart from package(s) rpart.
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weka.Bagging_REPTree (21)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.SMO_PolyKernel (13)
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.AdaBoostM1_DecisionStump (15)
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.Bagging_RandomForest (10)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.MathParameter (1)
Weka implementation of MathParameter
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weka.J48 (35)
Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
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weka.AdaBoostM1 (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.SMO (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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moa.HoeffdingTree (6)
Moa implementation of HoeffdingTree
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weka.SMO_RBFKernel (5)
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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mlr.classif.rpart (1)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.randomForest.filtered.oversampled (2)
Learner mlr.classif.randomForest.filtered.oversampled from package(s) mlr, randomForestSRC, randomForest.
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weka.SMO_PolyKernel (14)
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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mlr.classif.randomForest.filtered (4)
Learner mlr.classif.randomForest.filtered from package(s) randomForestSRC, randomForest.
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mlr.classif.rpart (3)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.rpart.preproc (2)
Learner mlr.classif.rpart.preproc from package(s) rpart.
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325b3936TESTSENTINEL999openml.sklearn.model_selection.KFold (1)
Automatically created sub-component.
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325b3936TESTSENTINEL999sklearn.pipeline.Pipeline(sklearn.preprocessing.data.OneHotEncoder__sklearn.preprocessing.data.StandardScaler__sklearn.pipeline.FeatureUnion(sklearn.preprocessing.data.PolynomialFeatures__sklearn.decomposition.pca.PCA)__sklearn.ensemble.weight_boosting.AdaBoostClassifier(sklearn.tree.tree.DecisionTreeClassifier)) (1)
Automatically created sub-component.
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325b3936TESTSENTINEL999sklearn.preprocessing.data.OneHotEncoder (1)
Automatically created sub-component.
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325b3936TESTSENTINEL999sklearn.preprocessing.data.StandardScaler (1)
Automatically created sub-component.
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325b3936TESTSENTINEL999sklearn.pipeline.FeatureUnion(sklearn.preprocessing.data.PolynomialFeatures__sklearn.decomposition.pca.PCA) (1)
Automatically created sub-component.
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325b3936TESTSENTINEL999sklearn.preprocessing.data.PolynomialFeatures (1)
Automatically created sub-component.
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325b3936TESTSENTINEL999sklearn.decomposition.pca.PCA (1)
Automatically created sub-component.
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325b3936TESTSENTINEL999sklearn.ensemble.weight_boosting.AdaBoostClassifier(sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created sub-component.
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325b3936TESTSENTINEL999sklearn.tree.tree.DecisionTreeClassifier (1)
Automatically created sub-component.
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mlr.classif.rpart.preproc.filtered (2)
Learner mlr.classif.rpart.preproc.filtered from package(s) randomForestSRC, rpart.
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mlr.classif.rpart.preproc (4)
Learner mlr.classif.rpart.preproc from package(s) rpart.
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classif.rpart (50)
Learner classif.rpart from package(s) rpart.
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mlr.classif.rpart.preproc.filtered (3)
Learner mlr.classif.rpart.preproc.filtered from package(s) rpart.
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weka.LinearRegression (7)
Weka implementation of LinearRegression
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mlr.classif.logreg.preproc.filtered (1)
Learner mlr.classif.logreg.preproc.filtered from package(s) stats.
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mlr.classif.logreg.preproc (1)
Learner mlr.classif.logreg.preproc from package(s) stats.
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mlr.classif.knn (1)
Learner mlr.classif.knn from package(s) class.
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mlr.classif.knn.preproc (1)
Learner mlr.classif.knn.preproc from package(s) class.
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mlr.classif.ctree (1)
Learner mlr.classif.ctree from package(s) party.
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mlr.classif.rpart (5)
Learner mlr.classif.rpart from package(s) rpart.
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weka.AdaBoostM1_DecisionStump (16)
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.Bagging_REPTree (22)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
0 runs0 likes0 downloads0 reach0 impact
weka.SMO_RBFKernel (6)
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.AdaBoostM1_J48 (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.AdaBoostM1_REPTree (11)
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.Bagging_DecisionStump (4)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.Bagging_IBk (4)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.Bagging_J48 (7)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.Bagging_JRip (4)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.Bagging_NaiveBayes (10)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.Bagging_RandomTree (10)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.LWL_DecisionStump (5)
Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003. C. Atkeson, A. Moore, S. Schaal (1996). Locally…
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weka.CVParameterSelection_ZeroR (4)
R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
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weka.ND_J48 (4)
Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. In: PKDD, 84-95, 2005. Eibe Frank, Stefan Kramer: Ensembles of nested dichotomies for…
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weka.LogitBoost_DecisionStump (7)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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weka.RandomCommittee_RandomTree (5)
Weka implementation of RandomCommittee
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weka.RandomSubSpace_REPTree (8)
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.LogitBoost_DecisionStump (8)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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mlr.classif.kknn.preproc (1)
Learner mlr.classif.kknn.preproc from package(s) !kknn.
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mlr.classif.kknn (1)
Learner mlr.classif.kknn from package(s) !kknn.
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weka.IBk (9)
D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
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mlr.classif.kknn.preproc (2)
Learner mlr.classif.kknn.preproc from package(s) !kknn.
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mlr.classif.kknn (2)
Learner mlr.classif.kknn from package(s) !kknn.
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mlr.classif.naiveBayes (1)
Learner mlr.classif.naiveBayes from package(s) e1071.
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weka.MultiSearch_J48 (3)
Weka implementation of MultiSearch
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weka.MultiSearch_SMO_RBFKernel (2)
Weka implementation of MultiSearch
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weka.SMO_RBFKernel (7)
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.MultiSearch_J48 (4)
Weka implementation of MultiSearch
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weka.MultiSearch_AdditiveRegression_REPTree (1)
Weka implementation of MultiSearch
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weka.AdditiveRegression_REPTree (3)
J.H. Friedman (1999). Stochastic Gradient Boosting.
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weka.MultiSearch_LogitBoost_REPTree (1)
Weka implementation of MultiSearch
0 runs0 likes2 downloads2 reach0 impact
weka.LogitBoost_REPTree (6)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes1 downloads1 reach0 impact
weka.MultiSearch_Logistic (1)
Weka implementation of MultiSearch
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weka.MultiSearch_RandomForest (1)
Weka implementation of MultiSearch
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weka.MultiSearch_MultilayerPerceptron (1)
Weka implementation of MultiSearch
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mlr.classif.naiveBayes.preproc (2)
Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
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mlr.classif.randomForest.preproc (2)
Learner mlr.classif.randomForest.preproc from package(s) randomForest.
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mlr.classif.randomForest (6)
Learner mlr.classif.randomForest from package(s) randomForest.
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mlr.classif.rpart.preproc (6)
Learner mlr.classif.rpart.preproc from package(s) rpart.
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mlr.classif.kknn.preproc (3)
Learner mlr.classif.kknn.preproc from package(s) !kknn.
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mlr.classif.kknn (4)
Learner mlr.classif.kknn from package(s) !kknn.
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mlr.classif.mlp.preproc (1)
Learner mlr.classif.mlp.preproc from package(s) RSNNS.
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mlr.classif.mlp (1)
Learner mlr.classif.mlp from package(s) RSNNS.
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mlr.classif.PART.preproc (1)
Learner mlr.classif.PART.preproc from package(s) RWeka.
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mlr.classif.PART (1)
Learner mlr.classif.PART from package(s) RWeka.
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mlr.classif.svm.preproc (1)
Learner mlr.classif.svm.preproc from package(s) e1071.
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mlr.classif.svm (2)
Learner mlr.classif.svm from package(s) e1071.
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mlr.classif.naiveBayes.preproc (3)
Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
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mlr.classif.naiveBayes (3)
Learner mlr.classif.naiveBayes from package(s) e1071.
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mlr.classif.rpart.preproc (7)
Learner mlr.classif.rpart.preproc from package(s) rpart.
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mlr.classif.rpart (7)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.kknn.preproc (4)
Learner mlr.classif.kknn.preproc from package(s) !kknn.
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mlr.classif.kknn (5)
Learner mlr.classif.kknn from package(s) !kknn.
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