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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._pca.PCA,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…
9 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._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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,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…
9 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.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…
9 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._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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.components.data_preprocessing.imputation.ImputationComponent,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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,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…
9 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.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…
9 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._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…
9 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.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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,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…
9 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.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…
9 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._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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,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…
9 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.tree._classes.DecisionTreeClassifier),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…
9 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.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…
9 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.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…
9 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.tree._classes.DecisionTreeClassifier),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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._pca.PCA,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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.Normalizer,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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,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…
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randomsubgroups._randomsubgroups.RandomSubgroupClassifier (1)
A random subgroups classifier. A random subgroups classifier is a meta estimator that fits a number subgroups on various sub-samples of the dataset. The sub-sample size is controlled with the…
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (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…
9 runs0 likes0 downloads0 reach0 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.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier) (4)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
9 runs0 likes0 downloads0 reach0 impact
tensorflow.keras.src.engine.sequential.Sequential.ba943640 (1)
Automatically created tensorflow flow.
9 runs0 likes0 downloads0 reach0 impact
classif.randomForest (51)
Learner classif.randomForest from package(s) randomForest.
8 runs0 likes1 downloads1 reach0 impact
classif.boosting (10)
Learner classif.boosting from package(s) adabag, rpart.
8 runs0 likes1 downloads1 reach0 impact
mlr.classif.logreg (1)
Learner mlr.classif.logreg from package(s) stats.
8 runs0 likes1 downloads1 reach0 impact
classif.plsdaCaret (7)
Learner classif.plsdaCaret from package(s) caret.
8 runs0 likes1 downloads1 reach0 impact
sklearn.tree.tree.DecisionTreeClassifier (2)
Flow generated by run_task
8 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (16)
Learner mlr.classif.rpart from package(s) rpart.
8 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_J48 (4)
Weka implementation of CostSensitiveClassifier
8 runs0 likes0 downloads0 reach7 impact
mlr.classif.develpartykit.ctree (7)
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 1089:…
8 runs0 likes0 downloads0 reach0 impact
mlr.classif.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 1089:…
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mlr.regr.develpartykit.ctree (2)
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:…
8 runs0 likes0 downloads0 reach0 impact
mlr.regr.develpartykit.cforest (2)
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:…
8 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.tuned (7)
Learner mlr.classif.rpart.tuned from package(s) rpart.
8 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,selectfrommodel=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach7 impact
weka.Bagging_J48 (9)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
8 runs0 likes0 downloads0 reach7 impact
weka.AdaBoostM1_RandomForest (6)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
8 runs0 likes0 downloads0 reach5 impact
sklearn.pipeline.Pipeline(svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,pca=sklearn.decomposition.pca.PCA,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_FilteredClassifier_FilteredClassifier_IBk (1)
Weka implementation of FilteredClassifier
8 runs0 likes0 downloads0 reach6 impact
weka.RandomizableFilteredClassifier_LMT (1)
Weka implementation of RandomizableFilteredClassifier
8 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach0 impact
keras.engine.sequential.Sequential.c43a9103 (1)
Automatically created keras flow.
8 runs0 likes0 downloads0 reach1 impact
classif.LiblineaRL2LogReg (2)
Learner classif.LiblineaRL2LogReg from package(s) LiblineaR.
8 runs0 likes1 downloads1 reach0 impact
classif.lqa (2)
Learner classif.lqa from package(s) lqa.
8 runs0 likes1 downloads1 reach0 impact
classif.mlp (2)
Learner classif.mlp from package(s) RSNNS.
8 runs0 likes1 downloads1 reach0 impact
weka.J48 (2)
Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
8 runs0 likes3 downloads3 reach7 impact
mlr.classif.rpart.tuned (17)
Learner mlr.classif.rpart.tuned from package(s) rpart.
8 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)),adaboostclassifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_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…
8 runs0 likes0 downloads0 reach0 impact
onnx_ml_pb2.ModelProto.2bd579db (1)
Automatically created ONNX flow.
8 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_FilteredClassifier_RandomForest (2)
Weka implementation of CostSensitiveClassifier
8 runs0 likes0 downloads0 reach4 impact
weka.CostSensitiveClassifier_AttributeSelectedClassifier_BayesNet (1)
Weka implementation of CostSensitiveClassifier
8 runs0 likes0 downloads0 reach0 impact
weka.Bagging_LMT (4)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
8 runs0 likes0 downloads0 reach8 impact
optimus_ml.optimizer.model_optimizer.ModelOptimizer(estimator=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier) (3)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach0 impact
rm.process.polynomial_by_binomial_classification_support_vector_machine (3)
A RapidMiner Workflow.
8 runs0 likes0 downloads0 reach0 impact
mlr.classif.C50.imputed.preproc (1)
Learner mlr.classif.C50.imputed.preproc from package(s) C50.
8 runs0 likes0 downloads0 reach0 impact
mlr.classif.dbnDNN.imputed.dummied.preproc (1)
Learner mlr.classif.dbnDNN.imputed.dummied.preproc from package(s) deepnet.
8 runs0 likes0 downloads0 reach0 impact
mlr.classif.earth.imputed.preproc (1)
Learner mlr.classif.earth.imputed.preproc from package(s) !earth.
8 runs0 likes0 downloads0 reach0 impact
mlr.classif.glmnet.imputed.preproc (1)
Learner mlr.classif.glmnet.imputed.preproc from package(s) glmnet.
8 runs0 likes0 downloads0 reach0 impact
mlr.classif.cvglmnet.imputed.preproc (1)
Learner mlr.classif.cvglmnet.imputed.preproc from package(s) glmnet.
8 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.imputed.preproc (1)
Learner mlr.classif.rpart.imputed.preproc from package(s) rpart.
8 runs0 likes0 downloads0 reach0 impact
mlr.classif.sda.imputed.dummied.preproc (1)
Learner mlr.classif.sda.imputed.dummied.preproc from package(s) sda.
8 runs0 likes0 downloads0 reach0 impact
mlr.classif.xgboost.imputed.dummied.preproc (1)
Learner mlr.classif.xgboost.imputed.dummied.preproc from package(s) xgboost.
8 runs0 likes0 downloads0 reach0 impact
weka.J48 (40)
Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
8 runs0 likes0 downloads0 reach3 impact
weka.RandomForest (14)
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
8 runs0 likes0 downloads0 reach5 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.StandardScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach7 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.RobustScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach5 impact
sklearn.pipeline.Pipeline(MinMaxScaler=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,svc=sklearn.pipeline.Pipeline(transformer=sklearn.preprocessing.data.StandardScaler,reduction=sklearn.decomposition.pca.PCA,estimator=sklearn.svm.classes.SVC),bnb=sklearn.naive_bayes.BernoulliNB) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach7 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,dimension_reduction=sklearn.decomposition.pca.PCA,scaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,rf=sklearn.ensemble.forest.RandomForestClassifier)) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach0 impact
weka.NaiveBayes (19)
George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
8 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(decisiontreeclassifier=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…
8 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,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…
8 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,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…
8 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.components.data_preprocessing.imputation.ImputationComponent,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._kernel_pca.KernelPCA,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.components.data_preprocessing.imputation.ImputationComponent,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.StandardScaler,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…
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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._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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,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…
8 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,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…
8 runs0 likes0 downloads0 reach0 impact