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weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_LADTree (1)
Weka implementation of AttributeSelectedClassifier
22 runs0 likes1 downloads1 reach0 impact
weka.IsolationForest (1)
Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou: Isolation Forest. In: ICDM, 413-422, 2008.
22 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_NBTree (1)
Weka implementation of AttributeSelectedClassifier
22 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_PaceRegression (1)
Weka implementation of AttributeSelectedClassifier
22 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_SPegasos (1)
Weka implementation of AttributeSelectedClassifier
22 runs0 likes1 downloads1 reach0 impact
weka.AdaBoostM1 (1)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
22 runs0 likes1 downloads1 reach0 impact
classif.nnet (7)
Learner classif.nnet from package(s) nnet.
22 runs0 likes1 downloads1 reach0 impact
mlr.classif.rpart.bagged (2)
Learner mlr.classif.rpart.bagged from package(s) rpart.
22 runs0 likes0 downloads0 reach0 impact
weka.REPTree (16)
Weka implementation of REPTree
22 runs0 likes0 downloads0 reach9 impact
sklearn.tree.tree.ExtraTreeClassifier (1)
Automatically created sub-component.
22 runs0 likes0 downloads0 reach21 impact
mlr.classif.qda.preproc (3)
Learner mlr.classif.qda.preproc from package(s) MASS.
22 runs0 likes0 downloads0 reach0 impact
weka.NaiveBayes (16)
George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
22 runs0 likes0 downloads0 reach18 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
22 runs0 likes0 downloads0 reach0 impact
mlr.classif.randomForest (29)
Learner mlr.classif.randomForest from package(s) randomForest.
22 runs0 likes0 downloads0 reach2 impact
sklearn.pipeline.Pipeline(Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
22 runs0 likes0 downloads0 reach4 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,dimreduct=sklearn.decomposition.pca.PCA,knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
22 runs0 likes0 downloads0 reach0 impact
weka.MultiSearch (1)
Weka implementation of MultiSearch
22 runs0 likes0 downloads0 reach21 impact
weka.AdaBoostM1 (4)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
22 runs0 likes0 downloads0 reach16 impact
weka.RotationForest (1)
Juan J. Rodriguez, Ludmila I. Kuncheva, Carlos J. Alonso (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10):1619-1630.…
22 runs0 likes0 downloads0 reach12 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,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…
22 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._fastica.FastICA,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…
22 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,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…
22 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,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…
22 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.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…
22 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._fastica.FastICA,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…
22 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.linear_model._stochastic_gradient.SGDClassifier),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…
22 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),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…
22 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,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…
22 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,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…
22 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.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…
22 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)),randomforestclassifier=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 fit…
22 runs0 likes0 downloads0 reach0 impact
weka.ADTree (1)
Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999.
21 runs0 likes1 downloads1 reach0 impact
weka.BayesNet_LAGDHillClimber (1)
Weka implementation of BayesNet
21 runs0 likes1 downloads1 reach0 impact
weka.BayesNet_TabuSearch (1)
Weka implementation of BayesNet
21 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_M5P (1)
Weka implementation of AttributeSelectedClassifier
21 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_RBFNetwork (1)
Weka implementation of AttributeSelectedClassifier
21 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_KStar (1)
Weka implementation of AttributeSelectedClassifier
21 runs0 likes1 downloads1 reach0 impact
weka.SimpleCart (1)
Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, California.
21 runs0 likes1 downloads1 reach0 impact
R.3.1.2.regress.svm (2)
The performanceEstimation standardWF using svm as the learner
21 runs0 likes1 downloads1 reach0 impact
weka.CostSensitiveClassifier_Bagging_REPTree (3)
Weka implementation of CostSensitiveClassifier
21 runs0 likes0 downloads0 reach21 impact
sklearn.ensemble.forest.RandomForestClassifier (11)
Flow generated by openml_run
21 runs0 likes6 downloads6 reach17 impact
sklearn.ensemble.weight_boosting.AdaBoostClassifier (1)
Flow generated by run_task
21 runs0 likes1 downloads1 reach0 impact
classif.randomForestSRCSyn (2)
Learner classif.randomForestSRCSyn from package(s) randomForestSRC.
21 runs0 likes1 downloads1 reach0 impact
classif.cforest (11)
Learner classif.cforest from package(s) party.
21 runs0 likes1 downloads1 reach0 impact
classif.lda (14)
Learner classif.lda from package(s) MASS.
21 runs0 likes1 downloads1 reach0 impact
classif.bdk (5)
Learner classif.bdk from package(s) kohonen.
21 runs0 likes1 downloads1 reach0 impact
mlr.classif.randomForest (19)
Learner mlr.classif.randomForest from package(s) randomForest.
21 runs0 likes0 downloads0 reach5 impact
weka.RandomizableFilteredClassifier_SMO_PolyKernel (1)
Weka implementation of RandomizableFilteredClassifier
21 runs0 likes0 downloads0 reach5 impact
sklearn.ensemble.forest.RandomForestClassifier (22)
Automatically created scikit-learn flow.
21 runs0 likes0 downloads0 reach17 impact
sklearn.dummy.DummyClassifier (6)
Automatically created scikit-learn flow.
21 runs0 likes0 downloads0 reach3 impact
sklearn.tree.tree.DecisionTreeClassifier (22)
Automatically created scikit-learn flow.
21 runs0 likes0 downloads0 reach21 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.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…
21 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC) (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…
21 runs0 likes0 downloads0 reach0 impact
sklearn.calibration.CalibratedClassifierCV(base_estimator=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)) (2)
Probability calibration with isotonic regression or logistic regression. The calibration is based on the :term:`decision_function` method of the `base_estimator` if it exists, else on…
21 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,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…
21 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,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…
21 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,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…
21 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._fastica.FastICA,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…
21 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,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…
21 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),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…
21 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,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…
21 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),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…
21 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),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…
21 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,kernelpca=sklearn.decomposition.kernel_pca.KernelPCA,randomforestclassifier=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 fit…
21 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(num=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=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…
21 runs0 likes0 downloads0 reach0 impact
weka.M5Rules (1)
Geoffrey Holmes, Mark Hall, Eibe Frank: Generating Rule Sets from Model Trees. In: Twelfth Australian Joint Conference on Artificial Intelligence, 1-12, 1999. Ross J. Quinlan: Learning with Continuous…
20 runs0 likes1 downloads1 reach0 impact
weka.GaussianProcesses_NormalizedPolyKernel (1)
David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
20 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_IsolationForest (1)
Weka implementation of AttributeSelectedClassifier
20 runs0 likes1 downloads1 reach0 impact
weka.Ridor (1)
Weka implementation of Ridor
20 runs0 likes1 downloads1 reach0 impact
weka.BayesNet_RepeatedHillClimber (3)
Weka implementation of BayesNet
20 runs0 likes1 downloads1 reach0 impact
moa.LeveragingBag_RuleClassifier (1)
Moa implementation of LeveragingBag
20 runs0 likes2 downloads2 reach1 impact
classif.OneR (2)
Learner classif.OneR from package(s) RWeka.
20 runs0 likes1 downloads1 reach0 impact
classif.PART (2)
Learner classif.PART from package(s) RWeka.
20 runs0 likes1 downloads1 reach0 impact
classif.avNNet (2)
Learner classif.avNNet from package(s) nnet.
20 runs0 likes1 downloads1 reach0 impact
classif.lda (7)
Learner classif.lda from package(s) MASS.
20 runs0 likes1 downloads1 reach0 impact
classif.multinom (2)
Learner classif.multinom from package(s) nnet.
20 runs0 likes1 downloads1 reach0 impact
classif.dbnDNN (6)
Learner classif.dbnDNN from package(s) deepnet.
20 runs0 likes1 downloads1 reach0 impact
classif.LiblineaRL2LogReg (7)
Learner classif.LiblineaRL2LogReg from package(s) LiblineaR.
20 runs0 likes1 downloads1 reach0 impact
classif.LiblineaRL1LogReg (7)
Learner classif.LiblineaRL1LogReg from package(s) LiblineaR.
20 runs0 likes1 downloads1 reach0 impact
classif.mlp (7)
Learner classif.mlp from package(s) RSNNS.
20 runs0 likes1 downloads1 reach0 impact
classif.saeDNN (6)
Learner classif.saeDNN from package(s) deepnet.
20 runs0 likes1 downloads1 reach0 impact
classif.xyf (5)
Learner classif.xyf from package(s) kohonen.
20 runs0 likes1 downloads1 reach0 impact
mlr.classif.ranger (1)
Learner mlr.classif.ranger from package(s) ranger.
20 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.bagged (1)
Learner mlr.classif.rpart.bagged from package(s) rpart.
20 runs0 likes0 downloads0 reach0 impact
mlr.classif.J48.bagged (1)
Learner mlr.classif.J48.bagged from package(s) RWeka.
20 runs0 likes0 downloads0 reach0 impact
mlr.classif.ctree.bagged (1)
Learner mlr.classif.ctree.bagged from package(s) party.
20 runs0 likes0 downloads0 reach0 impact
mlr.classif.J48.bagged (4)
Learner mlr.classif.J48.bagged from package(s) RWeka.
20 runs0 likes0 downloads0 reach0 impact
mlr.classif.ctree.bagged (4)
Learner mlr.classif.ctree.bagged from package(s) party.
20 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_RandomForest (3)
Weka implementation of CostSensitiveClassifier
20 runs0 likes0 downloads0 reach18 impact
mlr.classif.develpartykit.ctree (2)
Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/95fb76a6c6c645dc5915754b739f2c00b12df542/rstuff/openml_ctree.R and devel partykit package revision 1034:…
20 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.lazy.IBk(weka.core.neighboursearch.LinearNNSearch(weka.core.EuclideanDistance)))) (1)
Weka implementation.
20 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.bayes.NaiveBayes) (1)
Weka implementation.
20 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (51)
Automatically created scikit-learn flow.
20 runs0 likes0 downloads0 reach20 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),decisiontreeclassifier=sklearn.tree.tree.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…
20 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier) (8)
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…
20 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.impute.SimpleImputer,fs=sklearn.feature_selection.univariate_selection.SelectPercentile,rf=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 fit…
20 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,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…
20 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),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…
20 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,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…
20 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.RobustScaler,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…
20 runs0 likes0 downloads0 reach0 impact