OpenML
Learner mlr.classif.glmnet from package(s) glmnet.
33 runs0 likes0 downloads0 reach31 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…
33 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…
33 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…
33 runs0 likes0 downloads0 reach0 impact
Weka implementation of ZeroR
32 runs0 likes1 downloads1 reach30 impact
Weka implementation of FilteredClassifier
32 runs0 likes0 downloads0 reach22 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…
32 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…
32 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…
32 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…
32 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…
32 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…
32 runs0 likes0 downloads0 reach0 impact
Learner classif.rpart from package(s) rpart.
31 runs0 likes3 downloads3 reach31 impact
Learner classif.randomForest from package(s) randomForest.
31 runs0 likes1 downloads1 reach0 impact
Learner classif.ctree from package(s) party.
31 runs0 likes1 downloads1 reach0 impact
Moa implementation of AccuracyWeightedEnsemble
31 runs0 likes1 downloads1 reach0 impact
Automatically created scikit-learn flow.
31 runs0 likes0 downloads0 reach28 impact
Automatically created scikit-learn flow.
31 runs0 likes0 downloads0 reach0 impact
Automatically created scikit-learn flow.
31 runs0 likes0 downloads0 reach14 impact
Learner mlr.classif.randomForest from package(s) randomForest.
31 runs0 likes0 downloads0 reach19 impact
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
31 runs0 likes2 downloads2 reach20 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…
31 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…
31 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…
31 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…
31 runs0 likes0 downloads0 reach0 impact
Moa implementation of StackingAttempt
30 runs0 likes2 downloads2 reach1 impact
Learner classif.rpart from package(s) rpart.
30 runs0 likes2 downloads2 reach14 impact
Weka implementation of AttributeSelectedClassifier
30 runs0 likes1 downloads1 reach0 impact
Learner classif.J48 from package(s) RWeka.
30 runs0 likes1 downloads1 reach0 impact
Learner mlr.classif.randomForest from package(s) randomForest.
30 runs0 likes1 downloads1 reach0 impact
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.
30 runs0 likes0 downloads0 reach28 impact
Weka implementation of CostSensitiveClassifier
30 runs0 likes0 downloads0 reach28 impact
Learner mlr.classif.kknn from package(s) !kknn.
30 runs0 likes0 downloads0 reach0 impact
Weka implementation of SimpleLinearRegression
30 runs0 likes1 downloads1 reach0 impact
Weka implementation of LinearRegression
30 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…
30 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…
30 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…
30 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…
30 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…
30 runs0 likes0 downloads0 reach0 impact
Learner classif.J48 from package(s) RWeka.
29 runs0 likes1 downloads1 reach0 impact
Learner classif.IBk from package(s) RWeka.
29 runs0 likes1 downloads1 reach0 impact
Flow generated by run_task
29 runs0 likes0 downloads0 reach27 impact
Weka implementation of AttributeSelectedClassifier
29 runs0 likes1 downloads1 reach0 impact
Moa implementation of LeveragingBag
29 runs0 likes1 downloads1 reach0 impact
A flow using smo for the POC.
29 runs0 likes1 downloads1 reach0 impact
R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
29 runs0 likes0 downloads0 reach29 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…
29 runs0 likes0 downloads0 reach24 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…
29 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…
29 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…
29 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…
29 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…
29 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…
29 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…
29 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…
29 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…
29 runs0 likes0 downloads0 reach0 impact
Learner classif.JRip from package(s) RWeka.
28 runs0 likes1 downloads1 reach0 impact
Moa implementation of StackingAttempt
28 runs0 likes2 downloads2 reach23 impact
Learner classif.rda from package(s) klaR.
28 runs0 likes1 downloads1 reach0 impact
Weka implementation of CostSensitiveClassifier
28 runs0 likes0 downloads0 reach28 impact
David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
28 runs0 likes1 downloads1 reach0 impact
Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand. Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the…
28 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…
28 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…
28 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…
28 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…
28 runs0 likes0 downloads0 reach0 impact
Moa implementation of AccuracyWeightedEnsemble
27 runs0 likes1 downloads1 reach0 impact
Learner classif.IBk from package(s) RWeka.
27 runs0 likes1 downloads1 reach0 impact
Moa implementation of StackingAttempt
27 runs0 likes2 downloads2 reach23 impact
Moa implementation of StackingAttempt
27 runs0 likes2 downloads2 reach25 impact
Moa implementation of StackingAttempt
27 runs0 likes2 downloads2 reach22 impact
Moa implementation of StackingAttemptV2
27 runs0 likes2 downloads2 reach24 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.
27 runs0 likes0 downloads0 reach24 impact
Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets. In: Proceedings of the 5th International Conferenceon Discovery Science, 153-164, 2002.
27 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…
27 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…
27 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…
27 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…
27 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…
27 runs0 likes0 downloads0 reach0 impact
Weka implementation of CostSensitiveClassifier
26 runs0 likes0 downloads0 reach26 impact
Learner classif.boosting from package(s) adabag, rpart.
26 runs0 likes1 downloads1 reach0 impact
Learner mlr.classif.rpart from package(s) rpart.
26 runs0 likes0 downloads0 reach4 impact
Learner mlr.classif.qda.preproc from package(s) MASS.
26 runs0 likes0 downloads0 reach0 impact
Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of…
26 runs0 likes1 downloads1 reach0 impact
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.
26 runs0 likes1 downloads1 reach0 impact
Weka implementation of AttributeSelectedClassifier
26 runs0 likes1 downloads1 reach0 impact
Weka implementation of RandomizableFilteredClassifier
26 runs0 likes0 downloads0 reach24 impact
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
26 runs0 likes2 downloads2 reach12 impact
An AdaBoost classifier. An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset…
26 runs0 likes0 downloads0 reach0 impact