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weka.LWL_RandomForest (3)
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…
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weka.ClassificationViaRegression_NaiveBayes (1)
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.IterativeClassifierOptimizer_AdditiveRegression_DecisionStump (3)
Weka implementation of IterativeClassifierOptimizer
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weka.AdditiveRegression_DecisionStump (5)
J.H. Friedman (1999). Stochastic Gradient Boosting.
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weka.Bagging_RandomTree (12)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.RandomTree (13)
Weka implementation of RandomTree
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weka.AdaBoostM1_RandomTree (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.LogitBoost_Bagging_DecisionStump (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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weka.ClassificationViaRegression_HoeffdingTree (1)
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.Bagging_IBk (5)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.Bagging_RandomSubSpace_SimpleLogistic (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.LogitBoost_Bagging_DecisionStump (2)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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weka.Bagging_LogitBoost_NaiveBayes (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.LogitBoost_NaiveBayes (3)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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weka.Bagging_CostSensitiveClassifier_RandomForest (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.AdditiveRegression_RandomForest (1)
J.H. Friedman (1999). Stochastic Gradient Boosting.
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weka.AdditiveRegression_REPTree (4)
J.H. Friedman (1999). Stochastic Gradient Boosting.
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weka.LogitBoost_RandomForest (2)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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sklearn.pipeline.Pipeline (1)
Flow generated by run_task
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sklearn.ensemble.forest.RandomTreesEmbedding (1)
Flow generated by run_task
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sklearn.pipeline.Pipeline(sklearn.preprocessing.imputation.Imputer,sklearn.preprocessing.data.OneHotEncoder,sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created sub-component.
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__main__.Model (1)
Flow generated by run_task
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sklearn.ensemble.forest.RandomForestClassifier (15)
Flow generated by run_task
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AutoSklearnFLow.AutoSklearnFlow (1)
Flow generated by run_task
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sklearn.neighbors.classification.KNeighborsClassifier (5)
Flow generated by run_task
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mlr.classif.develpartykit.ctree (4)
Please use the mlr add-on code and devel partykit package revision 1078: https://r-forge.r-project.org/scm/viewvc.php/pkg/devel/?root=partykit
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mlr.classif.xgboost (2)
Learner mlr.classif.xgboost from package(s) xgboost.
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mlr.classif.develpartykit.ctree (6)
Please use the mlr add-on code and devel partykit package revision 1078: https://r-forge.r-project.org/scm/viewvc.php/pkg/devel/?root=partykit
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mlr.regr.develpartykit.ctree (1)
Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/master/rstuff/openml_newctree/new_ctree_mlr.R and devel partykit package revision 1082:…
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mlr.classif.rpart.imputed.filtered (3)
Learner mlr.classif.rpart.imputed.filtered from package(s) rpart.
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mlr.classif.develpartykit.cforest (1)
Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/master/rstuff/openml_newctree/new_ctree_mlr.R and devel partykit package revision 1089:…
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mlr.classif.develpartykit.cforest (2)
Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/master/rstuff/openml_newctree/new_ctree_mlr.R and devel partykit package revision 1089:…
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mlr.regr.develpartykit.cforest (1)
Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/master/rstuff/openml_newctree/new_ctree_mlr.R and devel partykit package revision 1089:…
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mlr.regr.develpartykit.cforest (4)
Please use the mlr add-on code https://github.com/HeidiSeibold/sandbox/blob/master/rstuff/openml_newctree/new_ctree_mlr.R and devel partykit package revision 1118:…
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sklearn.linear_model.base.LinearRegression (1)
Automatically created sub-component.
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sklearn.svm.classes.LinearSVC (1)
Automatically created sub-component.
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sklearn.linear_model.logistic.LogisticRegression (2)
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sklearn.tree.tree.DecisionTreeClassifier (5)
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sklearn.pipeline.Pipeline(steps__scale=sklearn.preprocessing.data.RobustScaler,steps__rf=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.preprocessing.data.RobustScaler (1)
Automatically created sub-component.
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sklearn.pipeline.Pipeline(steps__imputer=sklearn.preprocessing.imputation.Imputer,steps__onehotencoder=sklearn.preprocessing.data.OneHotEncoder,steps__zerocount=tpot.operators.preprocessors.zero_count.ZeroCount,steps__logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)
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sklearn.preprocessing.imputation.Imputer (3)
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sklearn.preprocessing.data.OneHotEncoder (3)
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tpot.operators.preprocessors.zero_count.ZeroCount (1)
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__main__.PipelineWrapper(estimator__logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)
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__main__.PipelineWrapper(estimator__bernoullinb=sklearn.naive_bayes.BernoulliNB) (1)
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sklearn.naive_bayes.BernoulliNB (1)
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sklearn.pipeline.Pipeline(steps__feature_selection=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.svm.classes.LinearSVC),steps__classification=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.svm.classes.LinearSVC) (1)
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sklearn.svm.classes.LinearSVC (2)
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sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,steps__Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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mlr.classif.randomForest (24)
Learner mlr.classif.randomForest from package(s) randomForest.
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sklearn.linear_model.perceptron.Perceptron (1)
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sklearn.ensemble.voting_classifier.VotingClassifier(estimators__rf_gini=sklearn.ensemble.forest.RandomForestClassifier,estimators__et_gini=sklearn.ensemble.forest.ExtraTreesClassifier,estimators__gb=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
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mlr.classif.C50.preproc (78)
Learner mlr.classif.C50.preproc from package(s) C50.
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sklearn.naive_bayes.GaussianNB (2)
Automatically created scikit-learn flow.
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openml.utils.preprocessing.ConditionalImputer (1)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.OneHotEncoder (4)
Automatically created scikit-learn flow.
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sklearn.feature_selection.variance_threshold.VarianceThreshold (1)
Automatically created scikit-learn flow.
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sklearn.feature_selection.univariate_selection.SelectPercentile (1)
Automatically created scikit-learn flow.
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sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(steps__scaling=sklearn.preprocessing.data.StandardScaler,steps__selector=sklearn.feature_selection.univariate_selection.GenericUnivariateSelect,steps__classifier=sklearn.svm.classes.SVC) (1)
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sklearn.preprocessing.data.StandardScaler (1)
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sklearn.feature_selection.univariate_selection.GenericUnivariateSelect (1)
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flows.AutoSklearnFLow.AutoSklearnFlow(preprocessor=sklearn.pipeline.Pipeline(steps__imputer=sklearn.preprocessing.imputation.Imputer,steps__onehotencoder=sklearn.preprocessing.data.OneHotEncoder,steps__variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold)) (1)
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sklearn.pipeline.Pipeline(steps__imputer=sklearn.preprocessing.imputation.Imputer,steps__onehotencoder=sklearn.preprocessing.data.OneHotEncoder,steps__variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold) (1)
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sklearn.feature_selection.variance_threshold.VarianceThreshold (2)
Automatically created sub-component.
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sklearn.ensemble.voting_classifier.VotingClassifier(estimators__rf=sklearn.ensemble.forest.RandomForestClassifier,estimators__et=sklearn.ensemble.forest.ExtraTreesClassifier,estimators__gb=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created sub-component.
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sklearn.ensemble.voting_classifier.VotingClassifier(estimators__clf1=sklearn.ensemble.forest.RandomForestClassifier,estimators__clf2=sklearn.ensemble.forest.ExtraTreesClassifier,estimators__clf3=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
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sklearn.ensemble.voting_classifier.VotingClassifier(estimators__clf1=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.ensemble.voting_classifier.VotingClassifier(estimators__random_state=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created sub-component.
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sklearn.ensemble.voting_classifier.VotingClassifier(clf1=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(rf=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.RandomForestClassifier,voting=sklearn.ensemble.forest.ExtraTreesClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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mlr.classif.rpart.imputed.filtered (4)
Learner mlr.classif.rpart.imputed.filtered from package(s) rpart.
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mlr.classif.rpart.imputed.filtered (5)
Learner mlr.classif.rpart.imputed.filtered from package(s) rpart.
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mlr.classif.featureless (1)
Learner mlr.classif.featureless from package(s) mlr.
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mlr.classif.randomForest (25)
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__variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,steps__deepforest=DeepForest.DeepForest) (1)
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DeepForest.DeepForest(preprocessor=sklearn.pipeline.Pipeline(steps__imputer=sklearn.preprocessing.imputation.Imputer,steps__onehotencoder=sklearn.preprocessing.data.OneHotEncoder,steps__variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold)) (1)
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sklearn.pipeline.Pipeline(steps__scaling=sklearn.preprocessing.data.StandardScaler,steps__classifier=sklearn.ensemble.voting_classifier.VotingClassifier(estimators__extra=sklearn.ensemble.forest.ExtraTreesClassifier,estimators__rf=sklearn.ensemble.forest.RandomForestClassifier,estimators__gradboost=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,estimators__xgboost=xgboost.sklearn.XGBClassifier)) (1)
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sklearn.ensemble.voting_classifier.VotingClassifier(estimators__extra=sklearn.ensemble.forest.ExtraTreesClassifier,estimators__rf=sklearn.ensemble.forest.RandomForestClassifier,estimators__gradboost=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,estimators__xgboost=xgboost.sklearn.XGBClassifier) (1)
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sklearn.pipeline.Pipeline(steps__standardscaler=sklearn.preprocessing.data.StandardScaler,steps__votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(estimators__extra=sklearn.ensemble.forest.ExtraTreesClassifier,estimators__rf=sklearn.ensemble.forest.RandomForestClassifier,estimators__gradboost=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,estimators__xgboost=xgboost.sklearn.XGBClassifier)) (1)
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(extra=sklearn.ensemble.forest.ExtraTreesClassifier,rf=sklearn.ensemble.forest.RandomForestClassifier,gradboost=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,xgboost=xgboost.sklearn.XGBClassifier)) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(extra=sklearn.ensemble.forest.ExtraTreesClassifier,rf=sklearn.ensemble.forest.RandomForestClassifier,gradboost=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,xgboost=xgboost.sklearn.XGBClassifier) (1)
Automatically created scikit-learn flow.
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mlr.classif.xgboost (3)
Learner mlr.classif.xgboost from package(s) xgboost.
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sklearn.pipeline.Pipeline(steps__Scaling=sklearn.preprocessing.data.StandardScaler,steps__features=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),steps__Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
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sklearn.pipeline.Pipeline(steps__StandardScaler=sklearn.preprocessing.data.StandardScaler,steps__SelectFromModel=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),steps__Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(steps__StandardScaler=sklearn.preprocessing.data.StandardScaler,steps__SelectFromModel=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),steps__RandomForestClassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(steps__Imputer=sklearn.preprocessing.imputation.Imputer,steps__Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(steps__scaling=sklearn.preprocessing.data.StandardScaler,steps__selector=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),steps__classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.pipeline.Pipeline(steps__scaling=sklearn.preprocessing.data.StandardScaler,steps__selector=sklearn.feature_selection.from_model.SelectFromModel(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),steps__RandomForestClassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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sklearn.feature_selection.univariate_selection.SelectKBest (1)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.MinMaxScaler (1)
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sklearn.ensemble.voting_classifier.VotingClassifier(extra=sklearn.ensemble.forest.ExtraTreesClassifier,rf=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(rf=sklearn.ensemble.forest.RandomForestClassifier,et=sklearn.ensemble.forest.ExtraTreesClassifier,gb=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.ExtraTreesClassifier,voting=sklearn.ensemble.forest.RandomForestClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=xgboost.sklearn.XGBClassifier)) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(estimators=sklearn.ensemble.forest.ExtraTreesClassifier,voting=sklearn.ensemble.forest.RandomForestClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=xgboost.sklearn.XGBClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(steps__imputer=sklearn.preprocessing.imputation.Imputer,steps__onehotencoder=sklearn.preprocessing.data.OneHotEncoder,steps__variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,steps__randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
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