DEVELOPMENT... OpenML
OpenML
ID's
Sort: most runs
10000 results
flow
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(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,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…
27 runs0 likes0 downloads0 reach0 impact
weka.BFTree (1)
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
weka.NBTree (1)
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.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_JRip (1)
Weka implementation of AttributeSelectedClassifier
26 runs0 likes1 downloads1 reach0 impact
weka.MultiBoostAB_LADTree (2)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
26 runs0 likes2 downloads2 reach12 impact
weka.CostSensitiveClassifier_RandomTree (2)
Weka implementation of CostSensitiveClassifier
26 runs0 likes0 downloads0 reach26 impact
classif.boosting (12)
Learner classif.boosting from package(s) adabag, rpart.
26 runs0 likes1 downloads1 reach0 impact
mlr.classif.rpart (23)
Learner mlr.classif.rpart from package(s) rpart.
26 runs0 likes0 downloads0 reach4 impact
mlr.classif.qda.preproc (2)
Learner mlr.classif.qda.preproc from package(s) MASS.
26 runs0 likes0 downloads0 reach0 impact
weka.RandomizableFilteredClassifier_Vote_Bagging (1)
Weka implementation of RandomizableFilteredClassifier
26 runs0 likes0 downloads0 reach24 impact
sklearn.pipeline.Pipeline(Scale=sklearn.preprocessing.data.Normalizer,Classifier=sklearn.ensemble.voting_classifier.VotingClassifier(lr=sklearn.linear_model.logistic.LogisticRegression,knn=sklearn.neighbors.classification.KNeighborsClassifier)) (1)
Automatically created scikit-learn flow.
26 runs0 likes0 downloads0 reach0 impact
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder)) (4)
Automatically created scikit-learn flow.
26 runs0 likes0 downloads0 reach0 impact
automlbenchmark_autosklearn (1)
Auto-sklearn as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
26 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.MultinomialNB),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…
26 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,estimator=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…
26 runs0 likes0 downloads0 reach0 impact
weka.M5P (1)
Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992. Y. Wang, I. H. Witten: Induction of model trees for…
25 runs0 likes1 downloads1 reach0 impact
weka.BayesNet_HillClimber (1)
Weka implementation of BayesNet
25 runs0 likes1 downloads1 reach0 impact
weka.ClassificationViaClustering_SimpleKMeans_EuclideanDistance (1)
Weka implementation of ClassificationViaClustering
25 runs0 likes1 downloads1 reach0 impact
weka.END_ND_J48 (1)
Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. In: PKDD, 84-95, 2005. Eibe Frank, Stefan Kramer: Ensembles of nested dichotomies for…
25 runs0 likes1 downloads1 reach0 impact
classif.JRip (3)
Learner classif.JRip from package(s) RWeka.
25 runs0 likes1 downloads1 reach0 impact
mlr.classif.develpartykit.ctree (3)
Please use the mlr add-on code and devel partykit package revision 1034:
25 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (38)
Learner mlr.classif.rpart from package(s) rpart.
25 runs0 likes0 downloads0 reach21 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,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…
25 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,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…
25 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,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…
25 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,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…
25 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,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…
25 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,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…
25 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=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…
25 runs0 likes0 downloads0 reach0 impact
weka.GaussianProcesses_PrecomputedKernelMatrixKernel (1)
David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
24 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_DecisionStump (1)
Weka implementation of AttributeSelectedClassifier
24 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_LMT (1)
Weka implementation of AttributeSelectedClassifier
24 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_REPTree (1)
Weka implementation of AttributeSelectedClassifier
24 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_ComplementNaiveBayes (1)
Weka implementation of AttributeSelectedClassifier
24 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_Logistic (1)
Weka implementation of AttributeSelectedClassifier
24 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_VotedPerceptron (1)
Weka implementation of AttributeSelectedClassifier
24 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_AdaBoostM1_DecisionStump (1)
Weka implementation of AttributeSelectedClassifier
24 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_LogitBoost_DecisionStump (1)
Weka implementation of AttributeSelectedClassifier
24 runs0 likes1 downloads1 reach0 impact
HubMiner.HIKNN (1)
HubMiner implementation of HIKNN
24 runs0 likes1 downloads1 reach0 impact
HubMiner.HwKNN (1)
HubMiner implementation of HwKNN
24 runs0 likes1 downloads1 reach0 impact
HubMiner.KNN (1)
HubMiner implementation of KNN
24 runs0 likes1 downloads1 reach0 impact
HubMiner.NHBNN (1)
HubMiner implementation of NHBNN
24 runs0 likes1 downloads1 reach0 impact
HubMiner.HFNN (1)
HubMiner implementation of HFNN
24 runs0 likes1 downloads1 reach0 impact
HubMiner.DWHFNN (1)
HubMiner implementation of DWHFNN
24 runs0 likes1 downloads1 reach0 impact
HubMiner.CBWkNN (1)
HubMiner implementation of CBWkNN
24 runs0 likes1 downloads1 reach0 impact
HubMiner.NWKNN (1)
HubMiner implementation of NWKNN
24 runs0 likes1 downloads1 reach0 impact
HubMiner.AKNN (1)
HubMiner implementation of AKNN
24 runs0 likes1 downloads1 reach0 impact
HubMiner.ANHBNN (1)
HubMiner implementation of ANHBNN
24 runs0 likes1 downloads1 reach0 impact
mlr.classif.develpartykit.ctree (8)
Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/master/rstuff/openml_newctree/new_ctree_mlr.R and devel partykit package revision 1118:…
24 runs0 likes0 downloads0 reach0 impact
mlr.classif.develpartykit.cforest (4)
Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/master/rstuff/openml_newctree/new_ctree_mlr.R and devel partykit package revision 1118:…
24 runs0 likes0 downloads0 reach0 impact
mlr.regr.develpartykit.ctree (3)
Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/master/rstuff/openml_newctree/new_ctree_mlr.R and devel partykit package revision 1118:…
24 runs0 likes0 downloads0 reach0 impact
mlr.regr.develpartykit.cforest (3)
Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/master/rstuff/openml_newctree/new_ctree_mlr.R and devel partykit package revision 1118:…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created sub-component.
24 runs0 likes0 downloads0 reach24 impact
sklearn.ensemble.forest.RandomForestClassifier (35)
Automatically created scikit-learn flow.
24 runs0 likes0 downloads0 reach24 impact
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder)) (2)
Automatically created scikit-learn flow.
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),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…
24 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._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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.RobustScaler,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…
24 runs0 likes0 downloads0 reach0 impact
weka.GaussianProcesses_Puk (1)
David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
23 runs0 likes1 downloads1 reach0 impact
weka.GaussianProcesses_StringKernel (1)
David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
23 runs0 likes1 downloads1 reach0 impact
weka.LWL_NaiveBayes (1)
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…
23 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_ADTree (1)
Weka implementation of AttributeSelectedClassifier
23 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_MLPClassifier (1)
Weka implementation of AttributeSelectedClassifier
23 runs0 likes1 downloads1 reach0 impact
weka.DTNB (1)
Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. In: Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS), 318-319, 2008.
23 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_ZeroR (1)
Weka implementation of AttributeSelectedClassifier
23 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_AdditiveRegression_DecisionStump (1)
Weka implementation of AttributeSelectedClassifier
23 runs0 likes1 downloads1 reach0 impact
weka.Prism (1)
J. Cendrowska (1987). PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies. 27(4):349-370.
23 runs0 likes1 downloads1 reach0 impact
classif.rpart (35)
Learner classif.rpart from package(s) rpart.
23 runs0 likes1 downloads1 reach23 impact
classif.avNNet (3)
Learner classif.avNNet from package(s) nnet.
23 runs0 likes1 downloads1 reach0 impact
classif.avNNet (11)
Learner classif.avNNet from package(s) nnet.
23 runs0 likes1 downloads1 reach0 impact
classif.multinom (8)
Learner classif.multinom from package(s) nnet.
23 runs0 likes1 downloads1 reach0 impact
mlr.classif.ranger (5)
Learner mlr.classif.ranger from package(s) ranger.
23 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.ExtraTreesClassifier (5)
Automatically created sub-component.
23 runs0 likes0 downloads0 reach22 impact
J48Bagging (1)
Just a 48Bagging workflow done with the KF.
23 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(missing=sklearn.impute.SimpleImputer,scale=sklearn.preprocessing.data.StandardScaler,predict=hpo4svm.hpo.SVR) (1)
Automatically created scikit-learn flow.
23 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,gaussiannb=sklearn.naive_bayes.GaussianNB) (2)
Automatically created scikit-learn flow.
23 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (2)
Classifier implementing the k-nearest neighbors vote.
23 runs0 likes0 downloads0 reach23 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,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…
23 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,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…
23 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,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…
23 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,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…
23 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,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…
23 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),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…
23 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (13)
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…
23 runs0 likes1 downloads1 reach18 impact
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier) (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…
23 runs0 likes0 downloads0 reach20 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neural_network._multilayer_perceptron.MLPClassifier) (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…
23 runs0 likes0 downloads0 reach0 impact
weka.LWL_J48 (1)
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…
22 runs0 likes1 downloads1 reach0 impact