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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Feature_Removal=sklearn.feature_selection.univariate_selection.SelectKBest,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.linear_model.stochastic_gradient.SGDClassifier (1)
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sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.decomposition.pca.PCA (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
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sklearn.ensemble.weight_boosting.AdaBoostClassifier (5)
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sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=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__.SecondGradientBoostingClassifier)) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(voting=sklearn.ensemble.forest.RandomForestClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=__main__.SecondGradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
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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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__main__.AUCLGBMClassifier (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.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__.AUCBooster)) (1)
Automatically created scikit-learn flow.
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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.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=aucbooster.AUCBooster)) (1)
Automatically created scikit-learn flow.
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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.model_selection._search.GridSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier) (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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Voter.Voter (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Feature_Removal=sklearn.feature_selection.univariate_selection.SelectKBest,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Feature_Removal=sklearn.feature_selection.univariate_selection.SelectKBest,Classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
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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.gaussian_process.gpc.GaussianProcessClassifier (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.ensemble.gradient_boosting.GradientBoostingClassifier) (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)
Automatically created scikit-learn flow.
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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)
Automatically created scikit-learn flow.
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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)
Automatically created scikit-learn flow.
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voter.Voter(rfc=sklearn.ensemble.forest.RandomForestClassifier,gbc=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,lclf=__main__.LightClassifier,xgb=aucbooster.AUCBooster) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,voter=voter2.Voter) (1)
Automatically created scikit-learn flow.
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voter2.Voter (1)
Automatically created scikit-learn flow.
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mlr.classif.rpart (31)
Learner mlr.classif.rpart from package(s) rpart.
97267 runs0 likes1 downloads1 reach93209 impact
mlr.classif.glmnet (4)
Learner mlr.classif.glmnet from package(s) glmnet.
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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.tree.tree.DecisionTreeClassifier)) (1)
Automatically created scikit-learn flow.
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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)
Automatically created scikit-learn flow.
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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)
Automatically created scikit-learn flow.
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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.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.ensemble.forest.RandomForestClassifier)) (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.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.svm.classes.SVC)) (1)
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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sklearn.linear_model.base.LinearRegression (2)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(pt1=helper.partial.PT1(transformer=helper.partial.IMP1),pt2=helper.partial.PT2(transformer=helper.partial.IMP2),pt3=helper.partial.PT3(transformer=sklearn.preprocessing.data.StandardScaler),onehotencoder=sklearn.preprocessing.data.OneHotEncoder,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)
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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weka.J48 (38)
Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
462 runs0 likes0 downloads0 reach448 impact
weka.LogitBoost_DecisionStump (11)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
4 runs0 likes0 downloads0 reach1 impact
weka.DecisionStump (8)
Weka implementation of DecisionStump
17 runs0 likes0 downloads0 reach17 impact
mlr.classif.ranger (7)
Learner mlr.classif.ranger from package(s) ranger.
2442 runs0 likes0 downloads0 reach1 impact
mlr.classif.kknn (10)
Learner mlr.classif.kknn from package(s) !kknn.
119520 runs0 likes0 downloads0 reach115750 impact
mlr.classif.svm (7)
Learner mlr.classif.svm from package(s) e1071.
74431 runs0 likes1 downloads1 reach72141 impact
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 (28)
Learner mlr.classif.randomForest from package(s) randomForest.
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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.rpart (4)
Learner mlr.regr.rpart from package(s) rpart.
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mlr.regr.lm (3)
Learner mlr.regr.lm from package(s) stats.
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mlr.classif.xgboost (4)
Learner mlr.classif.xgboost from package(s) xgboost.
3387 runs0 likes0 downloads0 reach1 impact
weka.SMO_RBFKernel (8)
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,…
126 runs0 likes0 downloads0 reach8 impact