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weka.FilteredClassifier_AttributeSelectedClassifier_LogitBoost_DecisionStump (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_MultiBoostAB_NaiveBayes (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_MultiBoostAB_IBk (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_MultiBoostAB_JRip (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_MultiBoostAB_OneR (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_MultiBoostAB_RandomTree (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_MultiBoostAB_REPTree (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_MultiBoostAB_DecisionStump (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_A1DE (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_DTNB (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_NBTree (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_LADTree (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_Dagging_DecisionStump (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_MultiBoostAB_JRip (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_MultiBoostAB_OneR (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_END_ND_J48 (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_RacedIncrementalLogitBoost_DecisionStump (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_A1DE (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_DTNB (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_NBTree (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_LADTree (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_Dagging_DecisionStump (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_END_ND_J48 (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_AttributeSelectedClassifier_RacedIncrementalLogitBoost_DecisionStump (1)
Weka implementation of FilteredClassifier
37 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier) (6)
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…
37 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.linear_model._stochastic_gradient.SGDClassifier),step_1=sklearn.naive_bayes.BernoulliNB) (1)
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…
37 runs0 likes0 downloads0 reach0 impact
weka.NaiveBayesMultinomial (1)
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
36 runs0 likes1 downloads1 reach0 impact
weka.CostSensitiveClassifier_Bagging_J48 (1)
Weka implementation of CostSensitiveClassifier
36 runs0 likes1 downloads1 reach35 impact
weka.CostSensitiveClassifier_NaiveBayes (2)
Weka implementation of CostSensitiveClassifier
36 runs0 likes1 downloads1 reach35 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.svm._classes.SVC) (1)
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…
36 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier (1)
Weka implementation of FilteredClassifier
36 runs0 likes0 downloads0 reach36 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis) (1)
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…
36 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,step_1=sklearn.ensemble._forest.RandomForestClassifier) (1)
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…
36 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.naive_bayes.GaussianNB) (2)
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…
36 runs0 likes0 downloads0 reach0 impact
classif.cvglmnet (2)
Learner classif.cvglmnet from package(s) glmnet.
35 runs0 likes1 downloads1 reach0 impact
classif.ksvm (8)
Learner classif.ksvm from package(s) kernlab.
35 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_DecisionTable_BestFirst (1)
Weka implementation of AttributeSelectedClassifier
35 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.naive_bayes.MultinomialNB) (1)
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…
35 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
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…
35 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier) (1)
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…
35 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier) (1)
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…
35 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.MultinomialNB),step_1=sklearn.ensemble._forest.RandomForestClassifier) (1)
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…
35 runs0 likes0 downloads0 reach0 impact
classif.randomForestSRC (10)
Learner classif.randomForestSRC from package(s) randomForestSRC.
34 runs0 likes1 downloads1 reach0 impact
classif.JRip (12)
Learner classif.JRip from package(s) RWeka.
34 runs0 likes1 downloads1 reach0 impact
classif.OneR (8)
Learner classif.OneR from package(s) RWeka.
34 runs0 likes1 downloads1 reach0 impact
classif.PART (8)
Learner classif.PART from package(s) RWeka.
34 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
34 runs0 likes0 downloads0 reach0 impact
weka.NaiveBayesMultinomialUpdateable (1)
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
34 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.ensemble._forest.RandomForestClassifier) (1)
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…
34 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,step_1=sklearn.svm._classes.SVC) (1)
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…
34 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (2)
Automatically created scikit-learn flow.
34 runs0 likes0 downloads0 reach26 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.linear_model._stochastic_gradient.SGDClassifier),step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
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…
34 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
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…
34 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),logisticregression=sklearn.linear_model._logistic.LogisticRegression) (1)
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…
34 runs0 likes0 downloads0 reach0 impact
classif.xgboost (9)
Learner classif.xgboost from package(s) xgboost.
33 runs0 likes1 downloads1 reach0 impact
classif.gbm (9)
Learner classif.gbm from package(s) gbm.
33 runs0 likes1 downloads1 reach0 impact
weka.AdaBoostM1_RandomForest (4)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
33 runs0 likes0 downloads0 reach33 impact
sklearn.ensemble.forest.RandomForestClassifier (10)
Flow generated by openml_run
33 runs0 likes7 downloads7 reach24 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,step_1=sklearn.ensemble._forest.RandomForestClassifier) (1)
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.FilteredClassifier_FilteredClassifier_RandomizableFilteredClassifier_RandomForest (1)
Weka implementation of FilteredClassifier
33 runs0 likes0 downloads0 reach0 impact
mlr.classif.glmnet (13)
Learner mlr.classif.glmnet from package(s) glmnet.
33 runs0 likes0 downloads0 reach31 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.tree._classes.DecisionTreeClassifier) (1)
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
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier) (1)
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
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._fastica.FastICA,step_1=sklearn.tree._classes.DecisionTreeClassifier) (1)
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
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._fastica.FastICA,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis) (1)
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
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.MultinomialNB),step_1=sklearn.naive_bayes.BernoulliNB) (1)
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
sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (4)
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 likes1 downloads1 reach0 impact
weka.ZeroR (12)
Weka implementation of ZeroR
32 runs0 likes1 downloads1 reach30 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
32 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.svm._classes.SVC) (1)
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
weka.FilteredClassifier_FilteredClassifier_Vote_RandomForest (1)
Weka implementation of FilteredClassifier
32 runs0 likes0 downloads0 reach22 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.tree._classes.DecisionTreeClassifier) (1)
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
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier) (1)
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
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,step_1=sklearn.ensemble._forest.RandomForestClassifier) (1)
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
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.tree._classes.DecisionTreeClassifier) (1)
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
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=sklearn.naive_bayes.BernoulliNB) (1)
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
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),step_1=sklearn.naive_bayes.BernoulliNB) (1)
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
classif.randomForest (57)
Learner classif.randomForest from package(s) randomForest.
31 runs0 likes1 downloads1 reach0 impact
classif.ctree (9)
Learner classif.ctree from package(s) party.
31 runs0 likes1 downloads1 reach0 impact
mlr.classif.randomForest (48)
Learner mlr.classif.randomForest from package(s) randomForest.
31 runs0 likes0 downloads0 reach19 impact
weka.MultiBoostAB_LWL_DecisionStump (2)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
31 runs0 likes2 downloads2 reach20 impact
moa.AccuracyWeightedEnsemble_WEKAClassifier_IBk (1)
Moa implementation of AccuracyWeightedEnsemble
31 runs0 likes1 downloads1 reach0 impact
classif.rpart (26)
Learner classif.rpart from package(s) rpart.
31 runs0 likes3 downloads3 reach31 impact
sklearn.dummy.DummyClassifier (2)
Automatically created scikit-learn flow.
31 runs0 likes0 downloads0 reach28 impact
sklearn.tree.tree.DecisionTreeClassifier (16)
Automatically created scikit-learn flow.
31 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
31 runs0 likes0 downloads0 reach0 impact
sklearn.dummy.DummyClassifier (7)
Automatically created scikit-learn flow.
31 runs0 likes0 downloads0 reach14 impact
keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Flatten,Dense,Dropout,Dense,Dropout,Dense) (2)
Automatically created scikit-learn flow.
31 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.ensemble._forest.RandomForestClassifier) (1)
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
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis) (1)
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
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
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
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,step_1=sklearn.ensemble._forest.RandomForestClassifier) (1)
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
weka.AttributeSelectedClassifier_Logistic (1)
Weka implementation of AttributeSelectedClassifier
30 runs0 likes1 downloads1 reach0 impact
classif.J48 (28)
Learner classif.J48 from package(s) RWeka.
30 runs0 likes1 downloads1 reach0 impact
mlr.classif.randomForest (4)
Learner mlr.classif.randomForest from package(s) randomForest.
30 runs0 likes1 downloads1 reach0 impact
weka.HoeffdingTree (6)
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.CostSensitiveClassifier_SimpleLogistic (2)
Weka implementation of CostSensitiveClassifier
30 runs0 likes0 downloads0 reach28 impact
weka.LinearRegression (4)
Weka implementation of LinearRegression
30 runs0 likes1 downloads1 reach0 impact
weka.SimpleLinearRegression (2)
Weka implementation of SimpleLinearRegression
30 runs0 likes1 downloads1 reach0 impact
moa.StackingAttempt_HoeffdingTree (1)
Moa implementation of StackingAttempt
30 runs0 likes2 downloads2 reach1 impact