Moa implementation of HoeffdingAdaptiveTree
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Moa implementation of RandomHoeffdingTree
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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
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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.
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Learner mlr.classif.develpartykit.cforest from package(s) partykit.
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Learner mlr.regr.ctree from package(s) party.
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Automatically created scikit-learn flow.
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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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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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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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S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.
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G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.
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Learner mlr.classif.gausspr.preproc from package(s) kernlab.
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Learner mlr.classif.cforest.preproc from package(s) party.
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George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
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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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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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The performanceEstimation standardWF using svm as the learner
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Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
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Automatically created sub-component.
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Learner mlr.classif.multinom.preproc from package(s) nnet.
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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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Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
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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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This class implements the regression task.
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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.C50.preproc from package(s) C50.
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Learner mlr.classif.earth.preproc from package(s) !earth.
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Learner mlr.classif.featureless.preproc from package(s) mlr.
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Learner mlr.classif.kknn.preproc from package(s) !kknn.
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Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
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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…
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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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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.
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Learner classif.extraTrees from package(s) extraTrees.
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Learner classif.hdrda from package(s) sparsediscrim.
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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…
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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.
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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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J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.
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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.
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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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Flow generated by openml_run
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Moa implementation of NaiveBayesMultinomial
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Moa implementation of StackingAttemptV2
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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.
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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
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Automatically created scikit-learn flow.
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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.
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Learner mlr.classif.glmnet from package(s) glmnet.
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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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Moa implementation of StackingAttemptV2
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Learner classif.IBk from package(s) RWeka.
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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…
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keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Flatten,Dense,Dropout,Dense,Dropout,Dense) (1)
Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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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…
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…
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Moa implementation of StackingAttemptV2
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Learner classif.nnTrain from package(s) deepnet.
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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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