DEVELOPMENT... OpenML
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weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_J48 (2)
Weka implementation of AttributeSelectedClassifier
14 runs0 likes1 downloads1 reach0 impact
weka.CostSensitiveClassifier_REPTree (4)
Weka implementation of CostSensitiveClassifier
14 runs0 likes0 downloads0 reach14 impact
classif.plr (7)
Learner classif.plr from package(s) stepPlr.
14 runs0 likes1 downloads1 reach0 impact
sklearn.neighbors.classification.KNeighborsClassifier (2)
Flow generated by run_task
14 runs0 likes3 downloads3 reach11 impact
classif.blackboost (7)
Learner classif.blackboost from package(s) mboost, party.
14 runs0 likes1 downloads1 reach0 impact
classif.logreg (11)
Learner classif.logreg from package(s) stats.
14 runs0 likes1 downloads1 reach0 impact
classif.plr (9)
Learner classif.plr from package(s) stepPlr.
14 runs0 likes1 downloads1 reach0 impact
classif.probit (7)
Learner classif.probit from package(s) stats.
14 runs0 likes1 downloads1 reach0 impact
classif.ada (10)
Learner classif.ada from package(s) ada.
14 runs0 likes1 downloads1 reach0 impact
mlr.classif.naiveBayes.preproc (6)
Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
14 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (27)
Learner mlr.classif.rpart from package(s) rpart.
14 runs0 likes0 downloads0 reach4 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,neuralnetwork=helper.nn.NeuralNetwork) (1)
Automatically created scikit-learn flow.
14 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_RandomForest (5)
Weka implementation of CostSensitiveClassifier
14 runs0 likes0 downloads0 reach14 impact
sklearn.naive_bayes.BernoulliNB (2)
Automatically created scikit-learn flow.
14 runs0 likes0 downloads0 reach0 impact
sklearn.svm.classes.SVC (15)
Automatically created scikit-learn flow.
14 runs0 likes0 downloads0 reach14 impact
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier (11)
Automatically created scikit-learn flow.
14 runs0 likes0 downloads0 reach12 impact
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier (13)
Automatically created scikit-learn flow.
14 runs0 likes0 downloads0 reach14 impact
mlr.costsens.classif.gbm (1)
Learner mlr.costsens.classif.gbm from package(s) gbm.
14 runs0 likes0 downloads0 reach0 impact
onnx.onnx_ONNX_RELEASE_ml_pb2.ModelProto.362eec6a (1)
Automatically created ONNX flow.
14 runs0 likes0 downloads0 reach1 impact
sklearn.pipeline.Pipeline(featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,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…
14 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,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…
14 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),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…
14 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),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…
14 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,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…
14 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.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…
14 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.StandardScaler,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…
14 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,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…
14 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.Binarizer,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…
14 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.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…
14 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,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…
14 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier) (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…
14 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (5)
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…
14 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._forest.RandomForestClassifier (12)
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…
14 runs0 likes0 downloads0 reach14 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)),decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor) (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…
14 runs0 likes0 downloads0 reach13 impact
moa.AMRules (1)
Moa implementation of AMRules
13 runs0 likes1 downloads1 reach0 impact
weka.BayesNet_RepeatedHillClimber (1)
Weka implementation of BayesNet
13 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_RBFClassifier (1)
Weka implementation of AttributeSelectedClassifier
13 runs0 likes1 downloads1 reach0 impact
moa.LeveragingBag_ORTO (1)
Moa implementation of LeveragingBag
13 runs0 likes1 downloads1 reach0 impact
classif.rpart (37)
Learner classif.rpart from package(s) rpart.
13 runs0 likes2 downloads2 reach13 impact
classif.LiblineaRL1LogReg (3)
Learner classif.LiblineaRL1LogReg from package(s) LiblineaR.
13 runs0 likes1 downloads1 reach0 impact
moa.BLAST (1)
Moa implementation of BLAST
13 runs0 likes1 downloads1 reach0 impact
classif.glmboost (6)
Learner classif.glmboost from package(s) mboost.
13 runs0 likes1 downloads1 reach0 impact
classif.mda (5)
Learner classif.mda from package(s) !mda.
13 runs0 likes1 downloads1 reach0 impact
voter2.Voter (1)
Automatically created scikit-learn flow.
13 runs0 likes1 downloads1 reach0 impact
weka.Bagging_RandomForest (12)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
13 runs0 likes0 downloads0 reach11 impact
weka.CostSensitiveClassifier_NaiveBayes (4)
Weka implementation of CostSensitiveClassifier
13 runs0 likes0 downloads0 reach11 impact
weka.RandomizableFilteredClassifier_J48 (1)
Weka implementation of RandomizableFilteredClassifier
13 runs0 likes0 downloads0 reach4 impact
weka.Bagging_FilteredClassifier_J48 (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
13 runs0 likes0 downloads0 reach0 impact
weka.RandomizableFilteredClassifier_Vote_RandomizableFilteredClassifier (1)
Weka implementation of RandomizableFilteredClassifier
13 runs0 likes0 downloads0 reach6 impact
mlr.classif.kknn (14)
Learner mlr.classif.kknn from package(s) !kknn.
13 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (19)
Automatically created scikit-learn flow.
13 runs0 likes0 downloads0 reach12 impact
mlr.classif.svm.imputed.preproc (1)
Learner mlr.classif.svm.imputed.preproc from package(s) e1071.
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (2)
Automatically created scikit-learn flow.
13 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (2)
Automatically created scikit-learn flow.
13 runs0 likes0 downloads0 reach2 impact
sklearn.pipeline.Pipeline(scale=sklearn.preprocessing.data.StandardScaler,predict=hpo4svm.hpo.sklearn.HPOSVM) (1)
Automatically created scikit-learn flow.
13 runs0 likes1 downloads1 reach0 impact
mlr.regr.gbm (2)
Learner mlr.regr.gbm from package(s) gbm.
13 runs0 likes0 downloads0 reach0 impact
mxnet.gluon.nn.basic_layers.HybridSequential.a3fc48b3 (1)
Automatically created MXNet flow.
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._fastica.FastICA,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MaxAbsScaler,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._fastica.FastICA,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier),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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,step_2=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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.feature_selection._variance_threshold.VarianceThreshold,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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…
13 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.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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,step_2=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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,step_2=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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._truncated_svd.TruncatedSVD,step_2=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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._truncated_svd.TruncatedSVD,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.StandardScaler,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…
13 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._forest.RandomForestClassifier),step_2=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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.Normalizer,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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier) (3)
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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (21)
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…
13 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neural_network._multilayer_perceptron.MLPClassifier) (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…
13 runs0 likes0 downloads0 reach0 impact
weka.PLSClassifier_PLSFilter (1)
Weka implementation of PLSClassifier
12 runs0 likes1 downloads1 reach0 impact
weka.RBFNetwork (1)
Weka implementation of RBFNetwork
12 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_HoeffdingTree (1)
Weka implementation of AttributeSelectedClassifier
12 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_LeastMedSq (1)
Weka implementation of AttributeSelectedClassifier
12 runs0 likes1 downloads1 reach0 impact
weka.CostSensitiveClassifier_REPTree (1)
Weka implementation of CostSensitiveClassifier
12 runs0 likes4 downloads4 reach8 impact
classif.LiblineaRL1LogReg (2)
Learner classif.LiblineaRL1LogReg from package(s) LiblineaR.
12 runs0 likes1 downloads1 reach0 impact
weka.CostSensitiveClassifier_Logistic (2)
Weka implementation of CostSensitiveClassifier
12 runs0 likes0 downloads0 reach12 impact
classif.OneR (3)
Learner classif.OneR from package(s) RWeka.
12 runs0 likes1 downloads1 reach0 impact
classif.PART (3)
Learner classif.PART from package(s) RWeka.
12 runs0 likes1 downloads1 reach0 impact
classif.avNNet (4)
Learner classif.avNNet from package(s) nnet.
12 runs0 likes1 downloads1 reach0 impact
classif.cforest (3)
Learner classif.cforest from package(s) party.
12 runs0 likes1 downloads1 reach0 impact
classif.ctree (3)
Learner classif.ctree from package(s) party.
12 runs0 likes1 downloads1 reach0 impact
classif.gbm (2)
Learner classif.gbm from package(s) gbm.
12 runs0 likes1 downloads1 reach0 impact
matrixnet_on_MagicTelescope_implementation (1)
implementation for uploading results of instrument *matrixnet* on dataset *MagicTelescope*
12 runs0 likes1 downloads1 reach0 impact