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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.components.data_preprocessing.imputation.ImputationComponent,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…
19 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.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…
19 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,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…
19 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.MultinomialNB),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…
19 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (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 fit…
19 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,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…
19 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model.logistic.LogisticRegression (1)
Flow generated by openml_run
18 runs0 likes2 downloads2 reach0 impact
mlr.classif.logreg (2)
Learner mlr.classif.logreg from package(s) stats.
18 runs0 likes1 downloads1 reach0 impact
weka.ComplementNaiveBayes (1)
Jason D. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger: Tackling the Poor Assumptions of Naive Bayes Text Classifiers. In: ICML, 616-623, 2003.
18 runs0 likes1 downloads1 reach0 impact
weka.IsotonicRegression (1)
Weka implementation of IsotonicRegression
18 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_NaiveBayes (1)
Weka implementation of AttributeSelectedClassifier
18 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_SimpleLogistic (1)
Weka implementation of AttributeSelectedClassifier
18 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_Bagging_REPTree (1)
Weka implementation of AttributeSelectedClassifier
18 runs0 likes1 downloads1 reach0 impact
weka.ConjunctiveRule (1)
Weka implementation of ConjunctiveRule
18 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_DTNB (1)
Weka implementation of AttributeSelectedClassifier
18 runs0 likes1 downloads1 reach0 impact
classif.logreg (5)
Learner classif.logreg from package(s) stats.
18 runs0 likes1 downloads1 reach0 impact
automlbenchmark_tpot (1)
TPOT as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
18 runs0 likes0 downloads0 reach0 impact
automlbenchmark_h2oautoml (1)
H2O AutoML 3.24.0.1 as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
18 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,decisiontreeclassifier=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…
18 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MaxAbsScaler,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…
18 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,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…
18 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Normalizer,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…
18 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) (1)
Automatically created scikit-learn flow.
18 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier (6)
Automatically created scikit-learn flow.
18 runs0 likes0 downloads0 reach17 impact
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.tree.tree.DecisionTreeClassifier) (2)
Automatically created scikit-learn flow.
18 runs0 likes0 downloads0 reach0 impact
sklearn.tree.tree.ExtraTreeClassifier (12)
Automatically created scikit-learn flow.
18 runs0 likes0 downloads0 reach17 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.StandardScaler,dimension_reduction=sklearn.decomposition.pca.PCA,classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
18 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
18 runs0 likes0 downloads0 reach17 impact
arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold)) (2)
Automatically created scikit-learn flow.
18 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,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…
18 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),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…
18 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),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…
18 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MaxAbsScaler,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…
18 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),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…
18 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,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…
18 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._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…
18 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.RobustScaler,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…
18 runs0 likes0 downloads0 reach0 impact
mlr3.classif.rpart (1)
Learner classif.rpart from package(s) mlr3, rpart.
18 runs0 likes0 downloads0 reach0 impact
tensorflow.keras.src.engine.functional.Functional.a92c89fa (1)
Automatically created tensorflow flow.
18 runs0 likes0 downloads0 reach0 impact
classif.cforest (9)
Learner classif.cforest from package(s) party.
17 runs0 likes1 downloads1 reach0 impact
classif.rotationForest (7)
Learner classif.rotationForest from package(s) rotationForest.
17 runs0 likes1 downloads1 reach0 impact
classif.sda (8)
Learner classif.sda from package(s) sda.
17 runs0 likes1 downloads1 reach0 impact
mlr.classif.J48 (2)
Learner mlr.classif.J48 from package(s) RWeka.
17 runs0 likes0 downloads0 reach0 impact
weka.DecisionStump (8)
Weka implementation of DecisionStump
17 runs0 likes0 downloads0 reach17 impact
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
17 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_FilteredClassifier_RandomForest (1)
Weka implementation of FilteredClassifier
17 runs0 likes0 downloads0 reach16 impact
weka.GaussianProcesses_PolyKernel (1)
David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
17 runs0 likes1 downloads1 reach0 impact
weka.LADTree (1)
Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.
17 runs0 likes1 downloads1 reach0 impact
weka.LWL_RandomTree (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…
17 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_RandomTree (1)
Weka implementation of AttributeSelectedClassifier
17 runs0 likes1 downloads1 reach0 impact
weka.Id3 (1)
R. Quinlan (1986). Induction of decision trees. Machine Learning. 1(1):81-106.
17 runs0 likes1 downloads1 reach0 impact
weka.Bagging_ADTree (2)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
17 runs0 likes2 downloads2 reach9 impact
ModelMultiplexer (1)
Learner ModelMultiplexer from package(s) e1071, ada, randomForest.
17 runs0 likes1 downloads1 reach0 impact
classif.bartMachine (1)
Learner classif.bartMachine from package(s) bartMachine.
17 runs0 likes1 downloads1 reach0 impact
classif.knn (1)
Learner classif.knn from package(s) class.
17 runs0 likes1 downloads1 reach0 impact
automlbenchmark_autoweka (1)
Auto-WEKA 2.6 as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
17 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.StandardScaler,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…
17 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_Bagging_AdaBoostM1_J48 (1)
Weka implementation of FilteredClassifier
17 runs0 likes0 downloads0 reach0 impact
weka.RandomizableFilteredClassifier_SimpleLogistic (1)
Weka implementation of RandomizableFilteredClassifier
17 runs0 likes0 downloads0 reach11 impact
decisionStump (1)
A flow using decisionStump for the POC.
17 runs0 likes1 downloads1 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (33)
Automatically created scikit-learn flow.
17 runs0 likes0 downloads0 reach15 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._logistic.LogisticRegressionCV) (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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._passive_aggressive.PassiveAggressiveClassifier) (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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._ridge.RidgeClassifierCV) (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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MaxAbsScaler,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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.linear_model._stochastic_gradient.SGDClassifier),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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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…
17 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.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…
17 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.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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,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…
17 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier (25)
Gradient Boosting for classification. GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage…
17 runs0 likes0 downloads0 reach0 impact
classif.xgboost (8)
Learner classif.xgboost from package(s) xgboost.
16 runs0 likes2 downloads2 reach10 impact
classif.glmboost (8)
Learner classif.glmboost from package(s) mboost.
16 runs0 likes1 downloads1 reach0 impact
weka.IBk (10)
D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
16 runs0 likes0 downloads0 reach5 impact
weka.FilteredClassifier_CostSensitiveClassifier_Bagging_LogitBoost_DecisionStump (1)
Weka implementation of FilteredClassifier
16 runs0 likes0 downloads0 reach0 impact
DeepForest.DeepForest (1)
Automatically created sub-component.
16 runs0 likes0 downloads0 reach0 impact
weka.SimpleLogistic (6)
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European Conference on Principles and…
16 runs0 likes0 downloads0 reach15 impact
weka.JRip (7)
William W. Cohen: Fast Effective Rule Induction. In: Twelfth International Conference on Machine Learning, 115-123, 1995.
16 runs0 likes0 downloads0 reach15 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.functions.Logistic)) (1)
Weka implementation.
16 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_M5Rules (1)
Weka implementation of AttributeSelectedClassifier
16 runs0 likes1 downloads1 reach0 impact
weka.ClassificationViaClustering_SimpleKMeans_EuclideanDistance (2)
Weka implementation of ClassificationViaClustering
16 runs0 likes1 downloads1 reach0 impact
moa.RandomRules_AMRules (1)
Moa implementation of RandomRules
16 runs0 likes1 downloads1 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)),svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
16 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
16 runs0 likes0 downloads0 reach0 impact
hyperpipes (1)
A flow using hyperpipes for the POC.
16 runs0 likes0 downloads0 reach0 impact
j48 (1)
A flow using j48 for the POC.
16 runs0 likes1 downloads1 reach0 impact
mlr.classif.naiveBayes (8)
Learner mlr.classif.naiveBayes from package(s) e1071.
16 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,clf=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
16 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=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…
16 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.ExtraTreeClassifier) (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…
16 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,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…
16 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,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…
16 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.cluster._agglomerative.FeatureAgglomeration,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…
16 runs0 likes0 downloads0 reach0 impact