DEVELOPMENT... OpenML
OpenML
ID's
Sort: most runs
10000 results
flow
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.linear_model._stochastic_gradient.SGDClassifier),step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.RobustScaler,step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier),step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.impute._base.MissingIndicator,step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._pca.PCA,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.feature_selection._variance_threshold.VarianceThreshold,step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.feature_selection._univariate_selection.SelectKBest,step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.impute._knn.KNNImputer,step_2=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,step_1=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.ColumnTransformer(date_encoder=sklearn.preprocessing._encoders.OrdinalEncoder,nomalization=sklearn.preprocessing._data.RobustScaler),clf=sklearn.neighbors._classification.KNeighborsClassifier) (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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors.classification.KNeighborsClassifier (43)
Classifier implementing the k-nearest neighbors vote.
5 runs0 likes0 downloads0 reach0 impact
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier) (2)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),sgdclassifier=sklearn.linear_model.stochastic_gradient.SGDClassifier) (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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model.logistic.LogisticRegression (39)
Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (16)
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…
5 runs0 likes0 downloads0 reach2 impact
sklearn.neighbors._classification.KNeighborsClassifier (11)
Classifier implementing the k-nearest neighbors vote.
5 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._forest.RandomForestClassifier (25)
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
5 runs0 likes0 downloads0 reach4 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestregressor=sklearn.ensemble._forest.RandomForestRegressor) (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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.LinearSVC) (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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer)),Classifier=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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardScaler),Classifier=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (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…
5 runs0 likes0 downloads0 reach0 impact
weka.Stacking_CostSensitiveClassifier_ZeroR (1)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
4 runs0 likes1 downloads1 reach0 impact
weka.Vote_REPTree (1)
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…
4 runs0 likes2 downloads2 reach3 impact
weka.LogitBoost_Bagging_REPTree (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
4 runs0 likes2 downloads2 reach3 impact
weka.AttributeSelectedClassifier_InfoGainAttributeEval_Ranker_RandomForest (1)
Weka implementation of AttributeSelectedClassifier
4 runs0 likes1 downloads1 reach0 impact
classif.rpart (16)
Learner classif.rpart from package(s) rpart.
4 runs0 likes1 downloads1 reach0 impact
classif.extraTrees (1)
Learner classif.extraTrees from package(s) extraTrees.
4 runs0 likes1 downloads1 reach0 impact
classif.IBk (2)
Learner classif.IBk from package(s) RWeka.
4 runs0 likes1 downloads1 reach0 impact
classif.LiblineaRL1LogReg (1)
Learner classif.LiblineaRL1LogReg from package(s) LiblineaR.
4 runs0 likes1 downloads1 reach0 impact
classif.nnTrain (1)
Learner classif.nnTrain from package(s) deepnet.
4 runs0 likes1 downloads1 reach0 impact
classif.rotationForest (1)
Learner classif.rotationForest from package(s) rotationForest.
4 runs0 likes1 downloads1 reach0 impact
moa.NaiveBayes (2)
No description
4 runs0 likes2 downloads2 reach2 impact
classif.ksvm (2)
Learner classif.ksvm from package(s) kernlab.
4 runs0 likes1 downloads1 reach0 impact
classif.naiveBayes (2)
Learner classif.naiveBayes from package(s) e1071.
4 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_GreedyStepwise_CostSensitiveClassifier_AdaBoostM1_REPTree (1)
Weka implementation of AttributeSelectedClassifier
4 runs0 likes1 downloads1 reach0 impact
classif.plsdaCaret (3)
Learner classif.plsdaCaret from package(s) caret.
4 runs0 likes1 downloads1 reach0 impact
classif.lda (10)
Learner classif.lda from package(s) MASS.
4 runs0 likes1 downloads1 reach0 impact
classif.binomial (8)
Learner classif.binomial from package(s) stats.
4 runs0 likes1 downloads1 reach0 impact
sklearn.neighbors.classification.KNeighborsClassifier (3)
Flow generated by run_task
4 runs0 likes1 downloads1 reach0 impact
mlr.classif.randomForest.filtered (2)
Learner mlr.classif.randomForest.filtered from package(s) randomForestSRC, randomForest.
4 runs0 likes1 downloads1 reach0 impact
classif.neuralnet (5)
Learner classif.neuralnet from package(s) neuralnet.
4 runs0 likes1 downloads1 reach0 impact
mlr.classif.naiveBayes.preproc (1)
Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
4 runs0 likes0 downloads0 reach0 impact
mlr.classif.ranger (4)
Learner mlr.classif.ranger from package(s) ranger.
4 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_Discretize_MultilayerPerceptron (1)
Weka implementation of FilteredClassifier
4 runs0 likes0 downloads0 reach0 impact
mlr.classif.randomForest (18)
Learner mlr.classif.randomForest from package(s) randomForest.
4 runs0 likes0 downloads0 reach0 impact
mlr.weightedclasses.classif.gbm.preproc (2)
Learner mlr.weightedclasses.classif.gbm.preproc from package(s) gbm.
4 runs0 likes0 downloads0 reach0 impact
weka.Vote_NaiveBayes (1)
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…
4 runs0 likes0 downloads0 reach0 impact
weka.Vote_J48 (2)
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…
4 runs0 likes0 downloads0 reach0 impact
weka.RandomForest (11)
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
4 runs0 likes0 downloads0 reach4 impact
weka.FilteredClassifier_CostSensitiveClassifier_SimpleLogistic (1)
Weka implementation of FilteredClassifier
4 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_CostSensitiveClassifier_RandomForest (2)
Weka implementation of FilteredClassifier
4 runs0 likes0 downloads0 reach2 impact
weka.CostSensitiveClassifier_Bagging_LogitBoost_RandomTree (1)
Weka implementation of CostSensitiveClassifier
4 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_Bagging_LogitBoost_DecisionStump (1)
Weka implementation of FilteredClassifier
4 runs0 likes0 downloads0 reach1 impact
weka.FilteredClassifier_CostSensitiveClassifier_Bagging_RandomForest (1)
Weka implementation of FilteredClassifier
4 runs0 likes0 downloads0 reach0 impact
weka.REPTree (17)
Weka implementation of REPTree
4 runs0 likes0 downloads0 reach4 impact
weka.CostSensitiveClassifier_AdaBoostM1_DecisionStump (1)
Weka implementation of CostSensitiveClassifier
4 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_IBk (1)
Weka implementation of CostSensitiveClassifier
4 runs0 likes0 downloads0 reach0 impact
mlr.weightedclasses.classif.xgboost.preproc.overbagged (1)
Learner mlr.weightedclasses.classif.xgboost.preproc.overbagged from package(s) xgboost.
4 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_FilteredClassifier_RandomForest (1)
Weka implementation of CostSensitiveClassifier
4 runs0 likes0 downloads0 reach0 impact
mlr.classif.randomForestSRC.preproc (1)
Learner mlr.classif.randomForestSRC.preproc from package(s) randomForestSRC.
4 runs0 likes0 downloads0 reach0 impact
mlr.classif.rda.preproc (1)
Learner mlr.classif.rda.preproc from package(s) klaR.
4 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (17)
Automatically created sub-component.
4 runs0 likes0 downloads0 reach4 impact
mlr.classif.C50.preproc (79)
Learner mlr.classif.C50.preproc from package(s) C50.
4 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.RandomForestClassifier,voting=sklearn.ensemble.forest.ExtraTreesClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach1 impact
sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.RandomForestClassifier,voting=xgboost.sklearn.XGBClassifier,weights=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach0 impact
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier)) (1)
Automatically created sub-component.
4 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_DecisionStump (11)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
4 runs0 likes0 downloads0 reach1 impact
weka.OneR (20)
R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
4 runs0 likes0 downloads0 reach3 impact
weka.RandomCommittee_RandomForest (1)
Weka implementation of RandomCommittee
4 runs0 likes0 downloads0 reach2 impact
weka.RandomTree (14)
Weka implementation of RandomTree
4 runs0 likes0 downloads0 reach4 impact
weka.CostSensitiveClassifier_SMO_PolyKernel (2)
Weka implementation of CostSensitiveClassifier
4 runs0 likes0 downloads0 reach3 impact
weka.Bagging_REPTree (26)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
4 runs0 likes0 downloads0 reach3 impact
weka.SMO_NormalizedPolyKernel (2)
J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning,…
4 runs0 likes0 downloads0 reach3 impact
weka.PART (6)
Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
4 runs0 likes0 downloads0 reach3 impact
weka.RandomForest (13)
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
4 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,kernelpca=sklearn.decomposition.kernel_pca.KernelPCA,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_CostSensitiveClassifier_Logistic (2)
Weka implementation of FilteredClassifier
4 runs0 likes0 downloads0 reach3 impact
weka.FilteredClassifier_FilteredClassifier_AttributeSelectedClassifier_IBk (1)
Weka implementation of FilteredClassifier
4 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_FilteredClassifier_SMO_PolyKernel (1)
Weka implementation of FilteredClassifier
4 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_Vote_SimpleLogistic (1)
Weka implementation of FilteredClassifier
4 runs0 likes0 downloads0 reach4 impact
weka.CostSensitiveClassifier_FilteredClassifier_SimpleLogistic (1)
Weka implementation of CostSensitiveClassifier
4 runs0 likes0 downloads0 reach3 impact
weka.RandomizableFilteredClassifier_Bagging_RandomForest (2)
Weka implementation of RandomizableFilteredClassifier
4 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_KStar (2)
Weka implementation of AttributeSelectedClassifier
4 runs0 likes0 downloads0 reach2 impact
weka.CostSensitiveClassifier_CostSensitiveClassifier_RandomForest (1)
Weka implementation of CostSensitiveClassifier
4 runs0 likes0 downloads0 reach0 impact
mlr.classif.ranger (12)
Learner mlr.classif.ranger from package(s) ranger.
4 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_LMT (2)
Weka implementation of AttributeSelectedClassifier
4 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (21)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach4 impact