OpenML
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21109 results
sklearn.gaussian_process.gpc.GaussianProcessClassifier (2)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach5 impact
sklearn.pipeline.Pipeline(PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
6 runs0 likes0 downloads0 reach5 impact
sklearn.pipeline.Pipeline(Classifier=sklearn.ensemble.forest.RandomForestClassifier) (3)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach5 impact
weka.DecisionTable (5)
Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
6 runs0 likes0 downloads0 reach5 impact
weka.FilteredClassifier (3)
Weka implementation of FilteredClassifier
19 runs0 likes0 downloads0 reach5 impact
sklearn.ensemble._forest.RandomForestClassifier (11)
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…
8 runs0 likes0 downloads0 reach5 impact
sklearn.neural_network.multilayer_perceptron.MLPClassifier (1)
Flow generated by run_task
10 runs0 likes3 downloads3 reach4 impact
sklearn.ensemble.bagging.BaggingClassifier (1)
Flow generated by run_task
5 runs0 likes2 downloads2 reach4 impact
mlr.classif.rpart (23)
Learner mlr.classif.rpart from package(s) rpart.
26 runs0 likes0 downloads0 reach4 impact
weka.RandomForest (11)
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
4 runs0 likes0 downloads0 reach4 impact
weka.CostSensitiveClassifier_Bagging_LogitBoost_DecisionStump (1)
Weka implementation of CostSensitiveClassifier
6 runs0 likes0 downloads0 reach4 impact
weka.CostSensitiveClassifier_LogitBoost_DecisionStump (1)
Weka implementation of CostSensitiveClassifier
7 runs0 likes0 downloads0 reach4 impact
weka.REPTree (17)
Weka implementation of REPTree
4 runs0 likes0 downloads0 reach4 impact
mlr.classif.rpart (27)
Learner mlr.classif.rpart from package(s) rpart.
14 runs0 likes0 downloads0 reach4 impact
sklearn.neighbors.classification.KNeighborsClassifier (6)
Automatically created sub-component.
6 runs0 likes0 downloads0 reach4 impact
sklearn.ensemble.forest.RandomForestClassifier (17)
Automatically created sub-component.
4 runs0 likes0 downloads0 reach4 impact
weka.RandomTree (14)
Weka implementation of RandomTree
4 runs0 likes0 downloads0 reach4 impact
weka.NaiveBayes (17)
George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
6 runs0 likes0 downloads0 reach4 impact
weka.RandomizableFilteredClassifier_Bagging_RandomForest (1)
Weka implementation of RandomizableFilteredClassifier
9 runs0 likes0 downloads0 reach4 impact
weka.RandomizableFilteredClassifier_J48 (1)
Weka implementation of RandomizableFilteredClassifier
13 runs0 likes0 downloads0 reach4 impact
sklearn.tree.tree.ExtraTreeClassifier (21)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach4 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach4 impact
weka.DecisionTable_BestFirst (3)
Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
5 runs0 likes5 downloads5 reach4 impact
weka.Bagging_SMO_PolyKernel (2)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
2878 runs0 likes2 downloads2 reach4 impact
weka.MultiBoostAB_IBk (2)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
41 runs0 likes2 downloads2 reach4 impact
classif.lda (1)
Learner classif.lda from package(s) MASS.
83 runs0 likes2 downloads2 reach4 impact
weka.A2DE (5)
Webb, Geoffrey I., Boughton, Janice, Zheng, Fei, Ting, Kai Ming, Salem, Houssam (2012). Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive…
145 runs0 likes2 downloads2 reach4 impact
sklearn.tree.tree.DecisionTreeClassifier (58)
A decision tree classifier.
4 runs0 likes0 downloads0 reach4 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=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…
6 runs0 likes0 downloads0 reach4 impact
weka.FilteredClassifier_Vote_SimpleLogistic (1)
Weka implementation of FilteredClassifier
4 runs0 likes0 downloads0 reach4 impact
weka.CostSensitiveClassifier_FilteredClassifier_RandomForest (2)
Weka implementation of CostSensitiveClassifier
8 runs0 likes0 downloads0 reach4 impact
weka.RandomizableFilteredClassifier_Vote_BayesNet (1)
Weka implementation of RandomizableFilteredClassifier
6 runs0 likes0 downloads0 reach4 impact
weka.CostSensitiveClassifier_SGD (1)
Weka implementation of CostSensitiveClassifier
5 runs0 likes0 downloads0 reach4 impact
sklearn.ensemble.forest.RandomForestClassifier (21)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach4 impact
sklearn.tree.tree.ExtraTreeClassifier (3)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach4 impact
sklearn.tree.tree.DecisionTreeClassifier (18)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach4 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,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach4 impact
weka.KStar (7)
John G. Cleary, Leonard E. Trigg: K*: An Instance-based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114, 1995.
5 runs0 likes0 downloads0 reach4 impact
sklearn.naive_bayes.GaussianNB (8)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach4 impact
sklearn.discriminant_analysis.LinearDiscriminantAnalysis (2)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach4 impact
sklearn.ensemble._forest.RandomForestClassifier (25)
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…
5 runs0 likes0 downloads0 reach4 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=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…
6 runs0 likes0 downloads0 reach4 impact
sklearn.ensemble._forest.RandomForestClassifier (34)
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…
4 runs0 likes0 downloads0 reach4 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=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 fit…
4 runs0 likes0 downloads0 reach4 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (15)
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 reach4 impact
sklearn.svm._classes.SVC (11)
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…
4 runs0 likes0 downloads0 reach4 impact
weka.AttributeSelectedClassifier_J48 (1)
Weka implementation of AttributeSelectedClassifier
140 runs0 likes0 downloads0 reach3 impact
weka.Bagging_LMT (3)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
138 runs0 likes0 downloads0 reach3 impact
weka.Bagging_RandomTree (11)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
3 runs0 likes0 downloads0 reach3 impact
weka.DecisionStump (7)
Weka implementation of DecisionStump
3 runs0 likes0 downloads0 reach3 impact
weka.Bagging_NaiveBayes (11)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
3 runs0 likes0 downloads0 reach3 impact
weka.HoeffdingTree (5)
Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 97-106, 2001.
7 runs0 likes0 downloads0 reach3 impact
weka.Bagging_SimpleLogistic (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
3 runs0 likes0 downloads0 reach3 impact
weka.CostSensitiveClassifier_ZeroR (3)
Weka implementation of CostSensitiveClassifier
5 runs0 likes0 downloads0 reach3 impact
weka.OneR (20)
R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
4 runs0 likes0 downloads0 reach3 impact
weka.RandomCommittee_RandomTree (7)
Weka implementation of RandomCommittee
6 runs0 likes0 downloads0 reach3 impact
weka.CostSensitiveClassifier_SMO_PolyKernel (2)
Weka implementation of CostSensitiveClassifier
4 runs0 likes0 downloads0 reach3 impact
weka.Bagging_REPTree (26)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
4 runs0 likes0 downloads0 reach3 impact
weka.SMO_NormalizedPolyKernel (2)
J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning,…
4 runs0 likes0 downloads0 reach3 impact
weka.PART (6)
Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
4 runs0 likes0 downloads0 reach3 impact
weka.RandomForest (13)
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
4 runs0 likes0 downloads0 reach3 impact
weka.FilteredClassifier_CostSensitiveClassifier_Logistic (2)
Weka implementation of FilteredClassifier
4 runs0 likes0 downloads0 reach3 impact
weka.FilteredClassifier_CostSensitiveClassifier_SMO_PolyKernel (1)
Weka implementation of FilteredClassifier
3 runs0 likes0 downloads0 reach3 impact
weka.AttributeSelectedClassifier_Logistic (2)
Weka implementation of AttributeSelectedClassifier
3 runs0 likes0 downloads0 reach3 impact
weka.RandomizableFilteredClassifier_LMT (1)
Weka implementation of RandomizableFilteredClassifier
8 runs0 likes0 downloads0 reach3 impact
xgboost.sklearn.XGBClassifier (7)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach3 impact
sklearn.neighbors.classification.KNeighborsClassifier (27)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach3 impact
sklearn.ensemble.forest.RandomForestClassifier (45)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach3 impact
sklearn.neighbors.classification.KNeighborsClassifier (35)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach3 impact
sklearn.ensemble.forest.RandomForestClassifier (54)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach3 impact
weka.VotedPerceptron (2)
Y. Freund, R. E. Schapire: Large margin classification using the perceptron algorithm. In: 11th Annual Conference on Computational Learning Theory, New York, NY, 209-217, 1998.
3 runs0 likes3 downloads3 reach3 impact
weka.Stacking_J48 (1)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
3 runs0 likes3 downloads3 reach3 impact
weka.Vote_REPTree (1)
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…
4 runs0 likes2 downloads2 reach3 impact
weka.LogitBoost_Bagging_REPTree (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
4 runs0 likes2 downloads2 reach3 impact
classif.rpart (36)
Learner classif.rpart from package(s) rpart.
3 runs0 likes2 downloads2 reach3 impact
weka.Bagging_LogitBoost_REPTree (2)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
3 runs0 likes0 downloads0 reach3 impact
weka.CostSensitiveClassifier_RandomCommittee_RandomTree (3)
Weka implementation of CostSensitiveClassifier
3 runs0 likes0 downloads0 reach3 impact
weka.OLM (4)
Arie Ben-David (1992). Automatic Generation of Symbolic Multiattribute Ordinal Knowledge-Based DSSs: methodology and Applications. Decision Sciences. 23:1357-1372.
272 runs0 likes3 downloads3 reach3 impact
classif.kknn (4)
Learner classif.kknn from package(s) !kknn.
134 runs0 likes2 downloads2 reach3 impact
sklearn.ensemble.forest.RandomForestClassifier (57)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier) (7)
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…
4 runs0 likes0 downloads0 reach3 impact
sklearn.tree._classes.DecisionTreeClassifier (4)
A decision tree classifier.
3 runs0 likes0 downloads0 reach3 impact
weka.CostSensitiveClassifier_FilteredClassifier_SMO_PolyKernel (1)
Weka implementation of CostSensitiveClassifier
6 runs0 likes0 downloads0 reach3 impact
weka.Bagging (4)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
3 runs0 likes0 downloads0 reach3 impact
weka.RandomizableFilteredClassifier_IBk (1)
Weka implementation of RandomizableFilteredClassifier
5 runs0 likes0 downloads0 reach3 impact
weka.CostSensitiveClassifier_FilteredClassifier_SimpleLogistic (1)
Weka implementation of CostSensitiveClassifier
4 runs0 likes0 downloads0 reach3 impact
weka.AdaBoostM1_LMT (4)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
3 runs0 likes0 downloads0 reach3 impact
weka.AttributeSelectedClassifier_SimpleLogistic (2)
Weka implementation of AttributeSelectedClassifier
3 runs0 likes0 downloads0 reach3 impact
sklearn.neural_network.multilayer_perceptron.MLPClassifier (5)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach3 impact
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier (8)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach3 impact
sklearn.dummy.DummyClassifier (6)
Automatically created scikit-learn flow.
21 runs0 likes0 downloads0 reach3 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.ZeroR (16)
Weka implementation of ZeroR
5 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach3 impact
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier) (3)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
6 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.RobustScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach3 impact