OpenML
Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
57 runs0 likes0 downloads0 reach0 impact
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European Conference on Principles and…
57 runs0 likes1 downloads1 reach0 impact
Weka implementation of DecisionStump
57 runs0 likes3 downloads3 reach2 impact
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
57 runs0 likes2 downloads2 reach50 impact
Learner classif.extraTrees from package(s) extraTrees.
57 runs0 likes1 downloads1 reach0 impact
Learner classif.hdrda from package(s) sparsediscrim.
57 runs0 likes1 downloads1 reach0 impact
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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Learner mlr.classif.ctree.preproc from package(s) party.
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Learner mlr.classif.gbm.preproc from package(s) gbm.
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Learner mlr.classif.glmnet.preproc from package(s) glmnet.
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Learner mlr.classif.ksvm.preproc from package(s) kernlab.
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Learner mlr.classif.randomForestSRC.preproc from package(s) randomForestSRC.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Learner mlr.classif.rda.preproc from package(s) klaR.
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Learner mlr.classif.svm.preproc from package(s) e1071.
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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…
56 runs0 likes1 downloads1 reach0 impact
J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.
56 runs0 likes2 downloads2 reach51 impact
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
56 runs0 likes4 downloads4 reach48 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
56 runs0 likes0 downloads0 reach0 impact
Flow generated by openml_run
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Moa implementation of NaiveBayesMultinomial
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Moa implementation of StackingAttemptV2
55 runs0 likes2 downloads2 reach46 impact
Learner mlr.classif.cvglmnet.preproc from package(s) glmnet.
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Learner mlr.classif.evtree.preproc from package(s) evtree.
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Learner mlr.classif.gausspr.preproc from package(s) kernlab.
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Learner mlr.classif.mda.preproc from package(s) !mda.
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Learner mlr.classif.nnet.preproc from package(s) nnet.
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Learner mlr.classif.randomForest.preproc from package(s) randomForest.
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Learner mlr.classif.RRF.preproc from package(s) RRF.
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Weka implementation of FilteredClassifier
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Weka implementation of MultiClassClassifierUpdateable
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Weka implementation of MultiScheme
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Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
55 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
55 runs0 likes0 downloads0 reach0 impact
Moa implementation of StackingAttempt
54 runs0 likes2 downloads2 reach46 impact
Weka implementation of RandomCommittee
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Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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Moa implementation of StackingAttempt
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Moa implementation of StackingAttemptV2
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Weka implementation of CostSensitiveClassifier
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E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
53 runs0 likes1 downloads1 reach0 impact
Learner mlr.classif.glmnet from package(s) glmnet.
53 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
53 runs0 likes0 downloads0 reach0 impact
Moa implementation of StackingAttemptV2
52 runs0 likes2 downloads2 reach33 impact
Learner classif.IBk from package(s) RWeka.
52 runs0 likes1 downloads1 reach0 impact
Automatically created scikit-learn flow.
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R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
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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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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…
52 runs0 likes1 downloads1 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
52 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
52 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
52 runs0 likes0 downloads0 reach0 impact
Moa implementation of StackingAttemptV2
51 runs0 likes2 downloads2 reach11 impact
Learner classif.nnTrain from package(s) deepnet.
51 runs0 likes1 downloads1 reach0 impact
Flow generated by run_task
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Learner mlr.classif.lda.preproc from package(s) MASS.
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Weka implementation of MultiClassClassifier
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le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.
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Weka implementation of AttributeSelectedClassifier
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Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
51 runs0 likes2 downloads2 reach46 impact
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
51 runs0 likes2 downloads2 reach46 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
51 runs0 likes0 downloads0 reach0 impact
Learner classif.randomForest from package(s) randomForest.
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Webb, Geoffrey I., Boughton, Janice, Zheng, Fei, Ting, Kai Ming, Salem, Houssam (2012). Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive…
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Weka implementation of FilteredClassifier
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Learner mlr.classif.cforest.preproc from package(s) party.
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Learner mlr.classif.multinom.preproc from package(s) nnet.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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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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Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: Second International Conference on Knoledge Discovery and Data Mining, 202-207, 1996.
50 runs0 likes2 downloads2 reach45 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
50 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
50 runs0 likes0 downloads0 reach0 impact
Learner classif.ranger from package(s) ranger.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Moa implementation of LeveragingBag
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Automatically created scikit-learn flow.
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Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 97-106, 2001.
49 runs0 likes3 downloads3 reach37 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
49 runs0 likes0 downloads0 reach0 impact
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
49 runs0 likes0 downloads0 reach0 impact
Learner classif.rpart from package(s) rpart.
48 runs0 likes1 downloads1 reach0 impact
Automatically created scikit-learn flow.
48 runs0 likes0 downloads0 reach40 impact
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
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Moa implementation of AccuracyWeightedEnsemble
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Weka implementation of FilteredClassifier
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Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
47 runs0 likes7 downloads7 reach46 impact
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…
47 runs0 likes2 downloads2 reach32 impact
Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999.
47 runs0 likes2 downloads2 reach44 impact
N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318. N. Littlestone (1989). Mistake bounds and logarithmic…
46 runs0 likes2 downloads2 reach37 impact