OpenML

moa.StackingAttempt_HoeffdingTree (1)

Moa implementation of StackingAttempt

30 runs0 likes2 downloads2 reach1 impact

openml.evaluation.area_under_roc_curve (1)

An implementation of the evaluation measure "area_under_roc_curve"

1 runs0 likes1 downloads1 reach1 impact

sklearn.tree.tree.DecisionTreeClassifier (55)

A decision tree classifier.

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sklearn.ensemble.forest.RandomForestClassifier (59)

A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…

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sklearn.naive_bayes.GaussianNB (19)

Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…

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sklearn.svm._classes.SVC (4)

C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of…

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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier) (17)

Automatically created scikit-learn flow.

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sklearn.tree.tree.DecisionTreeClassifier (43)

Automatically created scikit-learn flow.

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mxnet.gluon.nn.basic_layers.HybridSequential.cce30325 (1)

Automatically created MXNet flow.

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mxnet.gluon.nn.basic_layers.HybridSequential.d2a63ca2 (1)

Automatically created MXNet flow.

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onnx.onnx_ONNX_RELEASE_ml_pb2.ModelProto.362eec6a (1)

Automatically created ONNX flow.

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sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier) (6)

Automatically created scikit-learn flow.

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weka.MultiSearch_IBk (4)

Weka implementation of MultiSearch

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weka.CostSensitiveClassifier_FilteredClassifier_AdaBoostM1_MultilayerPerceptron (1)

Weka implementation of CostSensitiveClassifier

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weka.AdaBoostM1_SMO_PolyKernel (4)

Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

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weka.AdaBoostM1_FilteredClassifier_J48 (1)

Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

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weka.RandomizableFilteredClassifier_Vote_RandomizableFilteredClassifier_RandomTree (1)

Weka implementation of RandomizableFilteredClassifier

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weka.AdaBoostM1_PART (1)

Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

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weka.RandomizableFilteredClassifier_Vote_RandomForest (1)

Weka implementation of RandomizableFilteredClassifier

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weka.AdaBoostM1_RandomTree (5)

Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

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weka.FilteredClassifier_CostSensitiveClassifier_SimpleLogistic (2)

Weka implementation of FilteredClassifier

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weka.Vote_ZeroR (5)

Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc.. J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE…

1 runs0 likes0 downloads0 reach1 impact

weka.CostSensitiveClassifier_AttributeSelectedClassifier_RandomForest (1)

Weka implementation of CostSensitiveClassifier

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weka.RandomizableFilteredClassifier_AdaBoostM1_RandomForest (1)

Weka implementation of RandomizableFilteredClassifier

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weka.RandomizableFilteredClassifier_Vote_ZeroR (1)

Weka implementation of RandomizableFilteredClassifier

3 runs0 likes0 downloads0 reach1 impact

weka.FilteredClassifier_CostSensitiveClassifier_J48 (1)

Weka implementation of FilteredClassifier

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__main__.PythonChallengeClassifier (1)

Automatically created scikit-learn flow.

3 runs0 likes0 downloads0 reach1 impact

sklearn.tree.tree.ExtraTreeClassifier (2)

Automatically created scikit-learn flow.

6 runs0 likes0 downloads0 reach1 impact

mlr.classif.glmnet (11)

Learner mlr.classif.glmnet from package(s) glmnet.

1121355 runs0 likes10 downloads10 reach1 impact

mlr.classif.ranger (13)

Learner mlr.classif.ranger from package(s) ranger.

1231352 runs0 likes54 downloads54 reach1 impact

sklearn.linear_model.logistic.LogisticRegression (6)

Automatically created scikit-learn flow.

3 runs0 likes0 downloads0 reach1 impact

weka.AttributeSelectedClassifier_RandomTree (2)

Weka implementation of AttributeSelectedClassifier

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mlr.classif.randomForest (31)

Learner mlr.classif.randomForest from package(s) randomForest.

10 runs0 likes0 downloads0 reach1 impact

sklearn.svm.classes.SVC (9)

Automatically created scikit-learn flow.

1 runs0 likes0 downloads0 reach1 impact

sklearn.ensemble.forest.RandomForestClassifier (24)

Automatically created scikit-learn flow.

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sklearn.neighbors.classification.KNeighborsClassifier (12)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (6)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (7)

Automatically created scikit-learn flow.

5 runs0 likes0 downloads0 reach1 impact

mlr.classif.glmnet (15)

Learner mlr.classif.glmnet from package(s) glmnet.

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sklearn.svm.classes.LinearSVC (5)

Automatically created scikit-learn flow.

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weka.MultilayerPerceptron (10)

Weka implementation of MultilayerPerceptron

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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (14)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(Classifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(StandardScaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.linear_model.logistic.LogisticRegression) (1)

Automatically created scikit-learn flow.

3 runs0 likes0 downloads0 reach1 impact

sklearn.ensemble.voting_classifier.VotingClassifier(rf=sklearn.ensemble.forest.RandomForestClassifier,knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(rf=sklearn.ensemble.forest.RandomForestClassifier) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(Classifier=sklearn.naive_bayes.GaussianNB) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)

Automatically created scikit-learn flow.

4 runs0 likes0 downloads0 reach1 impact

sklearn.ensemble.forest.ExtraTreesClassifier (8)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)

Automatically created scikit-learn flow.

7 runs0 likes0 downloads0 reach1 impact

sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(svc=sklearn.svm.classes.SVC) (2)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (2)

Automatically created scikit-learn flow.

2 runs0 likes0 downloads0 reach1 impact

sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (2)

Automatically created scikit-learn flow.

6 runs0 likes0 downloads0 reach1 impact

sklearn.ensemble.voting_classifier.VotingClassifier(bg=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.neighbors.classification.KNeighborsClassifier),clf=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)) (1)

Automatically created scikit-learn flow.

2 runs0 likes0 downloads0 reach1 impact

sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)

Automatically created scikit-learn flow.

2 runs0 likes0 downloads0 reach1 impact

sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,rf=sklearn.ensemble.forest.RandomForestClassifier) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(reduce_dim=sklearn.decomposition.pca.PCA,clf=sklearn.svm.classes.SVC) (1)

Automatically created scikit-learn flow.

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sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.RobustScaler,Classifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)

Automatically created scikit-learn flow.

9 runs0 likes0 downloads0 reach1 impact

sklearn.pipeline.Pipeline(functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer,kerasclassifier=keras.wrappers.scikit_learn.KerasClassifier) (1)

Automatically created scikit-learn flow.

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weka.RandomSubSpace (1)

Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL…

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weka.DecisionStump (10)

Weka implementation of DecisionStump

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weka.RandomTree (17)

Weka implementation of RandomTree

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sklearn.linear_model.logistic.LogisticRegression (13)

Automatically created scikit-learn flow.

1 runs0 likes0 downloads0 reach1 impact

sklearn.ensemble.forest.RandomForestClassifier (38)

Automatically created scikit-learn flow.

3 runs0 likes0 downloads0 reach1 impact

sklearn.tree._classes.DecisionTreeClassifier (30)

A decision tree classifier.

1 runs0 likes0 downloads0 reach1 impact

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (22)

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…

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sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestregressor=sklearn.ensemble._forest.RandomForestRegressor) (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…

9 runs0 likes0 downloads0 reach1 impact

sklearn.ensemble._forest.RandomForestClassifier (40)

A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…

3 runs0 likes0 downloads0 reach1 impact

mlr.classif.rpart (60)

Learner mlr.classif.rpart from package(s) rpart.

1 runs0 likes0 downloads0 reach1 impact

sklearn.tree._classes.DecisionTreeClassifier (10)

A decision tree classifier.

1 runs0 likes0 downloads0 reach1 impact

sklearn.ensemble._forest.RandomForestClassifier (6)

A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…

2 runs0 likes0 downloads0 reach1 impact

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (10)

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…

2 runs0 likes0 downloads0 reach1 impact

amlb_constantpredictor (2)

This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark.git Repository commit: f0086d1bd6488395413bfe1f6caf8f9a34b8910d constantpredictor version: stable

3 runs0 likes0 downloads0 reach1 impact

amlb_constantpredictor (3)

This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark.git Repository commit: 75567510ce887b7b8aa857b9a1f9f29d1775813c constantpredictor version: stable

6 runs0 likes0 downloads0 reach1 impact

sklearn.tree._classes.DecisionTreeClassifier (23)

A decision tree classifier.

1 runs0 likes0 downloads0 reach1 impact

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,decisiontreeclassifier=sklearn.tree._classes.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…

16 runs0 likes0 downloads0 reach1 impact

sklearn.ensemble._forest.RandomForestClassifier (13)

A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…

3 runs0 likes0 downloads0 reach1 impact

sklearn.linear_model._base.LinearRegression (3)

Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset,…

8 runs0 likes0 downloads0 reach1 impact

sklearn.tree._classes.DecisionTreeClassifier (28)

A decision tree classifier.

1 runs0 likes0 downloads0 reach1 impact

classif.randomForest (51)

Learner classif.randomForest from package(s) randomForest.

8 runs0 likes1 downloads1 reach0 impact

sklearn.linear_model.logistic.LogisticRegression (1)

Flow generated by openml_run

18 runs0 likes2 downloads2 reach0 impact

classif.randomForest (52)

Learner classif.randomForest from package(s) randomForest.