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sklearn.ensemble.voting_classifier.VotingClassifier(voting=sklearn.ensemble.forest.RandomForestClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=xgboost.sklearn.XGBClassifier) (1)
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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,select=sklearn.feature_selection.univariate_selection.SelectPercentile,scaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.svm.classes.SVC) (1)
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mlr.classif.featureless (2)
Learner mlr.classif.featureless from package(s) mlr.
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mlr.classif.randomForest (26)
Learner mlr.classif.randomForest from package(s) randomForest.
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sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,steps__Feature_Removal=sklearn.feature_selection.univariate_selection.SelectKBest,steps__Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,steps__Feature_Removal=sklearn.feature_selection.univariate_selection.SelectKBest,steps__Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
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sklearn.pipeline.Pipeline(steps__preprocessing=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(steps__select=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,steps__select=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(steps__preprocessing=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__RandomForestClassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(steps__preprocessing=sklearn.preprocessing.imputation.Imputer,steps__OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,steps__Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(steps__preprocessing=sklearn.preprocessing.imputation.Imputer,steps__Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.decomposition.pca.PCA (1)
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__main__.SecondGradientBoostingClassifier (1)
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(steps__OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,steps__scaler=sklearn.preprocessing.data.StandardScaler,steps__select=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__model=sklearn.svm.classes.SVC)) (1)
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sklearn.pipeline.Pipeline(steps__OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,steps__scaler=sklearn.preprocessing.data.StandardScaler,steps__select=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__model=sklearn.svm.classes.SVC) (1)
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sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__scaler=sklearn.preprocessing.data.MinMaxScaler,steps__classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
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sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__Classifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
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sklearn.ensemble.voting_classifier.VotingClassifier(voting=sklearn.ensemble.forest.RandomForestClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=__main__.AUCBooster) (1)
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__main__.AUCBooster (1)
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sklearn.ensemble.voting_classifier.VotingClassifier(voting=sklearn.ensemble.forest.RandomForestClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=aucbooster.AUCBooster) (1)
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aucbooster.AUCBooster (1)
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sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__preprocessing=sklearn.preprocessing.data.StandardScaler,steps__select=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(steps__OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,steps__scaler=sklearn.preprocessing.data.StandardScaler,steps__select=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__model=sklearn.linear_model.logistic.LogisticRegression)) (1)
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sklearn.pipeline.Pipeline(steps__OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,steps__scaler=sklearn.preprocessing.data.StandardScaler,steps__select=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__model=sklearn.linear_model.logistic.LogisticRegression) (1)
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression) (1)
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.voting_classifier.VotingClassifier(estimators__rf=sklearn.ensemble.forest.RandomForestClassifier,estimators__gb=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)) (1)
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sklearn.ensemble.voting_classifier.VotingClassifier(estimators__rf=sklearn.ensemble.forest.RandomForestClassifier,estimators__gb=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
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mlr.classif.rpart.imputed.filtered (6)
Learner mlr.classif.rpart.imputed.filtered from package(s) rpart.
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sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__preprocessing=sklearn.preprocessing.data.StandardScaler,steps__select=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__preprocessing=sklearn.preprocessing.data.RobustScaler,steps__select=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__classifier=sklearn.neighbors.classification.KNeighborsClassifier)) (1)
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sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__preprocessing=sklearn.preprocessing.data.RobustScaler,steps__select=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
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sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__preprocessing=sklearn.preprocessing.data.StandardScaler,steps__features=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__classifier=sklearn.svm.classes.SVC) (1)
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sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__preprocessing=sklearn.preprocessing.data.StandardScaler,steps__features=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(steps__feature_selection=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__scaler=sklearn.preprocessing.data.MinMaxScaler,steps__classifier=sklearn.ensemble.forest.RandomForestClassifier)) (1)
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sklearn.pipeline.Pipeline(steps__feature_selection=sklearn.feature_selection.univariate_selection.SelectPercentile,steps__scaler=sklearn.preprocessing.data.MinMaxScaler,steps__classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(steps__preprocessing=sklearn.preprocessing.data.StandardScaler,steps__classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
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sklearn.pipeline.Pipeline(steps__StandardScaler=sklearn.preprocessing.data.StandardScaler,steps__Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
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sklearn.pipeline.Pipeline(steps__Scaler=sklearn.preprocessing.data.StandardScaler,steps__Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
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sklearn.pipeline.Pipeline(steps__Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(steps__scaler=sklearn.preprocessing.data.MinMaxScaler,steps__classifier=sklearn.ensemble.forest.RandomForestClassifier)) (1)
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sklearn.pipeline.Pipeline(steps__scaler=sklearn.preprocessing.data.MinMaxScaler,steps__classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(voting=sklearn.ensemble.forest.RandomForestClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=__main__.LightClassifier)) (1)
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sklearn.ensemble.voting_classifier.VotingClassifier(voting=sklearn.ensemble.forest.RandomForestClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=__main__.LightClassifier) (1)
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__main__.LightClassifier (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,voter=voter.Voter(rfc=sklearn.ensemble.forest.RandomForestClassifier,gbc=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,lclf=__main__.LightClassifier,xgb=aucbooster.AUCBooster)) (1)
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voter.Voter(rfc=sklearn.ensemble.forest.RandomForestClassifier,gbc=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,lclf=__main__.LightClassifier,xgb=aucbooster.AUCBooster) (1)
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,voter=voter2.Voter) (1)
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.tree.tree.DecisionTreeClassifier (7)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Imputer=openml.utils.preprocessing.ConditionalImputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)) (1)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.gradient_boosting.GradientBoostingClassifier (4)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Imputer=openml.utils.preprocessing.ConditionalImputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neural_network.multilayer_perceptron.MLPClassifier)) (1)
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.neural_network.multilayer_perceptron.MLPClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.neural_network.multilayer_perceptron.MLPClassifier (2)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Imputer=openml.utils.preprocessing.ConditionalImputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.GridSearchCV(estimator=sklearn.neighbors.unsupervised.NearestNeighbors)) (1)
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.neighbors.unsupervised.NearestNeighbors) (1)
Automatically created scikit-learn flow.
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sklearn.neighbors.unsupervised.NearestNeighbors (1)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.svm.classes.SVC (6)
Automatically created scikit-learn flow.
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helper.partial.PT1(transformer=helper.partial.IMP1) (1)
Automatically created scikit-learn flow.
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helper.partial.IMP1 (1)
Automatically created scikit-learn flow.
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helper.partial.PT2(transformer=helper.partial.IMP2) (1)
Automatically created scikit-learn flow.
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helper.partial.IMP2 (1)
Automatically created scikit-learn flow.
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helper.partial.PT3(transformer=sklearn.preprocessing.data.StandardScaler) (1)
Automatically created scikit-learn flow.
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mlr.classif.randomForest (27)
Learner mlr.classif.randomForest from package(s) randomForest.
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mlr.classif.logreg (5)
Learner mlr.classif.logreg from package(s) stats.
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mlr.classif.logreg (6)
Learner mlr.classif.logreg from package(s) stats.
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mlr.classif.lda (1)
Learner mlr.classif.lda from package(s) MASS.
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mlr.classif.randomForest.filtered (6)
Learner mlr.classif.randomForest.filtered from package(s) randomForest.
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mlr.classif.randomForest.oversampled (2)
Learner mlr.classif.randomForest.oversampled from package(s) mlr, randomForest.
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mlr.classif.randomForest.imputed (1)
Learner mlr.classif.randomForest.imputed from package(s) randomForest.
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mlr.classif.randomForest.filtered.oversampled (4)
Learner mlr.classif.randomForest.filtered.oversampled from package(s) mlr, randomForest.
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mlr.regr.lm (3)
Learner mlr.regr.lm from package(s) stats.
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weka.RBFKernel (5)
Weka implementation of RBFKernel
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sklearn.preprocessing.data.PolynomialFeatures (1)
Automatically created scikit-learn flow.
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sklearn.decomposition.kernel_pca.KernelPCA (1)
Automatically created scikit-learn flow.
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weka.SimpleLinearRegression (6)
Weka implementation of SimpleLinearRegression
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weka.PolyKernel (11)
Weka implementation of PolyKernel
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weka.LinearRegression (9)
Weka implementation of LinearRegression
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weka.M5P (6)
Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992. Y. Wang, I. H. Witten: Induction of model trees for…
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weka.GaussianProcesses_PolyKernel (11)
David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
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weka.RandomCommittee_M5P (1)
Weka implementation of RandomCommittee
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weka.NormalizedPolyKernel (7)
Weka implementation of NormalizedPolyKernel
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weka.IsolationForest (4)
Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou: Isolation Forest. In: ICDM, 413-422, 2008.
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weka.LOF (1)
Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jorg Sander (2000). LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record. 29(2):93-104.
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weka.SMO (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,…
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weka.CostSensitiveClassifier_LinearRegression (3)
Weka implementation of CostSensitiveClassifier
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weka.Vote_NaiveBayes (3)
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…
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weka.HNB (8)
H. Zhang, L. Jiang, J. Su: Hidden Naive Bayes. In: Twentieth National Conference on Artificial Intelligence, 919-924, 2005.
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helper.dual_imputer.DualImputer (1)
Automatically created scikit-learn flow.
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helper.nn.NeuralNetwork (1)
Automatically created scikit-learn flow.
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weka.M5Rules (5)
Geoffrey Holmes, Mark Hall, Eibe Frank: Generating Rule Sets from Model Trees. In: Twelfth Australian Joint Conference on Artificial Intelligence, 1-12, 1999. Ross J. Quinlan: Learning with Continuous…
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weka.ClassificationViaRegression_J48 (2)
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
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weka.AttributeSelectedClassifier_Stacking_SMO (1)
Weka implementation of AttributeSelectedClassifier
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__main__.LR (1)
Automatically created scikit-learn flow.
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