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.
62 runs0 likes6 downloads6 reach60 impact
J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.
62 runs0 likes2 downloads2 reach51 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…
62 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…
62 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…
62 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…
62 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…
62 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…
62 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…
62 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…
62 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…
62 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…
62 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…
62 runs0 likes0 downloads0 reach0 impact
Learner classif.J48 from package(s) RWeka.
61 runs0 likes1 downloads1 reach0 impact
Learner mlr.classif.lda.preproc from package(s) MASS.
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Moa implementation of AdaHoeffdingOptionTree
61 runs0 likes2 downloads2 reach60 impact
Moa implementation of HoeffdingOptionTree
61 runs0 likes2 downloads2 reach34 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…
61 runs0 likes0 downloads0 reach14 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…
61 runs0 likes0 downloads0 reach0 impact
Moa implementation of ASHoeffdingTree
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Moa implementation of HoeffdingAdaptiveTree
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Moa implementation of RandomHoeffdingTree
60 runs0 likes0 downloads0 reach0 impact
Moa implementation of MajorityClass
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Moa implementation of Perceptron
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Moa implementation of HoeffdingOptionTree
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Moa implementation of kNNwithPAW
60 runs0 likes0 downloads0 reach0 impact
Moa implementation of HoeffdingTree
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Moa implementation of DecisionStump
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Moa implementation of AccuracyWeightedEnsemble
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Learner mlr.regr.develpartykit.cforest from package(s) partykit.
60 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.develpartykit.cforest from package(s) partykit.
60 runs0 likes0 downloads0 reach0 impact
Automatically created scikit-learn flow.
60 runs0 likes0 downloads0 reach0 impact
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
60 runs0 likes2 downloads2 reach59 impact
S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.
60 runs0 likes1 downloads1 reach60 impact
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
60 runs0 likes2 downloads2 reach49 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…
60 runs0 likes0 downloads0 reach0 impact
Learner mlr.regr.ctree from package(s) party.
60 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.gausspr.preproc from package(s) kernlab.
59 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.cforest.preproc from package(s) party.
59 runs0 likes0 downloads0 reach0 impact
George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
59 runs0 likes1 downloads1 reach0 impact
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
59 runs0 likes2 downloads2 reach54 impact
The performanceEstimation standardWF using svm as the learner
59 runs0 likes1 downloads1 reach0 impact
G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.
59 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…
59 runs0 likes0 downloads0 reach0 impact
Automatically created sub-component.
58 runs0 likes0 downloads0 reach57 impact
Learner mlr.classif.multinom.preproc from package(s) nnet.
58 runs0 likes0 downloads0 reach0 impact
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.
58 runs0 likes1 downloads1 reach0 impact
Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
58 runs0 likes1 downloads1 reach0 impact
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
58 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…
58 runs0 likes0 downloads0 reach50 impact
This class implements the regression task.
58 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.C50.preproc from package(s) C50.
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Learner mlr.classif.earth.preproc from package(s) !earth.
57 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.featureless.preproc from package(s) mlr.
57 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.kknn.preproc from package(s) !kknn.
57 runs0 likes0 downloads0 reach0 impact
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
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…
57 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…
57 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…
57 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…
57 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.ctree.preproc from package(s) party.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.gbm.preproc from package(s) gbm.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.glmnet.preproc from package(s) glmnet.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.ksvm.preproc from package(s) kernlab.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.randomForestSRC.preproc from package(s) randomForestSRC.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.rda.preproc from package(s) klaR.
56 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.svm.preproc from package(s) e1071.
56 runs0 likes0 downloads0 reach0 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…
56 runs0 likes1 downloads1 reach0 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
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
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
56 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…
56 runs0 likes0 downloads0 reach0 impact
keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,Activation,Conv2D,MaxPooling2D,Activation,Dropout,Conv2D,Flatten,Dense,Activation,Dense,Activation) (5)
Automatically created scikit-learn flow.
56 runs0 likes0 downloads0 reach0 impact
keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,ZeroPadding2D,Conv2D,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,AveragePooling2D,Flatten,Dense) (2)
Automatically created scikit-learn flow.
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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.
55 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.mda.preproc from package(s) !mda.
55 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.nnet.preproc from package(s) nnet.
55 runs0 likes0 downloads0 reach0 impact
Learner mlr.classif.randomForest.preproc from package(s) randomForest.
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Learner mlr.classif.RRF.preproc from package(s) RRF.
55 runs0 likes0 downloads0 reach0 impact
Weka implementation of FilteredClassifier
55 runs0 likes1 downloads1 reach0 impact
Weka implementation of MultiClassClassifierUpdateable
55 runs0 likes1 downloads1 reach0 impact
Weka implementation of MultiScheme
55 runs0 likes1 downloads1 reach0 impact
Flow generated by openml_run
55 runs0 likes5 downloads5 reach17 impact
Moa implementation of NaiveBayesMultinomial
55 runs0 likes2 downloads2 reach47 impact
Moa implementation of StackingAttemptV2
55 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…
55 runs0 likes0 downloads0 reach0 impact
Automatically created scikit-learn flow.
55 runs0 likes0 downloads0 reach53 impact
Automatically created scikit-learn flow.
54 runs0 likes0 downloads0 reach0 impact
Weka implementation of RandomCommittee
54 runs0 likes1 downloads1 reach0 impact
Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
54 runs0 likes1 downloads1 reach0 impact
Moa implementation of StackingAttempt
54 runs0 likes2 downloads2 reach46 impact
Automatically created tensorflow flow.
54 runs0 likes0 downloads0 reach0 impact