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mlr.classif.ranger (13)
Learner mlr.classif.ranger from package(s) ranger.
1231352 runs0 likes54 downloads54 reach1 impact
SubgroupDiscovery (1)
Subgroup Discovery search algorithm.
1171422 runs1 likes10 downloads11 reach0 impact
mlr.classif.glmnet (11)
Learner mlr.classif.glmnet from package(s) glmnet.
1121355 runs0 likes10 downloads10 reach1 impact
mlr.classif.xgboost (9)
Learner mlr.classif.xgboost from package(s) xgboost.
1029849 runs1 likes21 downloads22 reach2 impact
mlr.classif.rpart (29)
Learner mlr.classif.rpart from package(s) rpart.
1028857 runs0 likes1 downloads1 reach1028856 impact
mlr.classif.svm (6)
Learner mlr.classif.svm from package(s) e1071.
937928 runs0 likes4 downloads4 reach937926 impact
mlr.classif.ranger (9)
Learner mlr.classif.ranger from package(s) ranger.
573666 runs0 likes3 downloads3 reach1 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
249475 runs1 likes4 downloads5 reach0 impact
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
138771 runs0 likes5 downloads5 reach0 impact
mlr.classif.kknn (10)
Learner mlr.classif.kknn from package(s) !kknn.
119520 runs0 likes0 downloads0 reach115750 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
109070 runs0 likes2 downloads2 reach0 impact
mlr.classif.glmnet (5)
Learner mlr.classif.glmnet from package(s) glmnet.
106003 runs0 likes3 downloads3 reach1 impact
mlr.classif.rpart (31)
Learner mlr.classif.rpart from package(s) rpart.
97267 runs0 likes1 downloads1 reach93209 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (2)
Automatically created scikit-learn flow.
84614 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (3)
Automatically created scikit-learn flow.
80829 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
76281 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
75800 runs0 likes2 downloads2 reach0 impact
mlr.classif.xgboost (6)
Learner mlr.classif.xgboost from package(s) xgboost.
75581 runs0 likes1 downloads1 reach1 impact
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)) (1)
Automatically created scikit-learn flow.
74642 runs0 likes2 downloads2 reach0 impact
mlr.classif.svm (7)
Learner mlr.classif.svm from package(s) e1071.
74431 runs0 likes1 downloads1 reach72141 impact
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
73428 runs0 likes1 downloads1 reach28 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (4)
Automatically created scikit-learn flow.
72428 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
68468 runs0 likes0 downloads0 reach0 impact
mlr.classif.ranger (16)
Learner mlr.classif.ranger from package(s) ranger.
64882 runs0 likes1 downloads1 reach64862 impact
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
63950 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
63192 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
59588 runs0 likes3 downloads3 reach0 impact
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
54522 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro) (1)
Automatically created scikit-learn flow.
53180 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
46654 runs0 likes0 downloads0 reach0 impact
weka.A1DE (4)
G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.
40298 runs0 likes11 downloads11 reach728 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),bernoullinb=sklearn.naive_bayes.BernoulliNB) (1)
Automatically created scikit-learn flow.
39207 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
36363 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
35708 runs1 likes3 downloads4 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (2)
Automatically created scikit-learn flow.
29304 runs0 likes1 downloads1 reach0 impact
mlr.classif.ranger (15)
Learner mlr.classif.ranger from package(s) ranger.
28167 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
24388 runs0 likes0 downloads0 reach0 impact
sklearn.svm.classes.SVC (32)
Automatically created scikit-learn flow.
17317 runs0 likes0 downloads0 reach17317 impact
mlr.classif.kknn (9)
Learner mlr.classif.kknn from package(s) !kknn.
16688 runs0 likes2 downloads2 reach16635 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),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…
15492 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.preproc (16)
Learner mlr.classif.rpart.preproc from package(s) rpart.
14284 runs0 likes6 downloads6 reach0 impact
weka.FilteredClassifier_Discretize_J48 (4)
Weka implementation of FilteredClassifier
13669 runs0 likes4 downloads4 reach144 impact
mlr.classif.xgboost (7)
Learner mlr.classif.xgboost from package(s) xgboost.
13577 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC) (2)
Automatically created scikit-learn flow.
13455 runs0 likes0 downloads0 reach0 impact
weka.LibSVM (2)
Yasser EL-Manzalawy (2005). WLSVM. URL http://www.cs.iastate.edu/~yasser/wlsvm/. Chih-Chung Chang, Chih-Jen Lin (2001). LIBSVM - A Library for Support Vector Machines. URL…
12795 runs0 likes10 downloads10 reach154 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
12377 runs0 likes0 downloads0 reach0 impact
weka.Bagging_REPTree (8)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
11893 runs0 likes4 downloads4 reach68 impact
weka.BayesNet_K2 (6)
Weka implementation of BayesNet
10761 runs0 likes5 downloads5 reach619 impact
weka.SMO_PolyKernel (11)
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,…
10467 runs0 likes6 downloads6 reach10428 impact
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)) (1)
Automatically created scikit-learn flow.
9868 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
9626 runs0 likes0 downloads0 reach0 impact
weka.RandomForest (5)
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
9544 runs0 likes6 downloads6 reach9433 impact
weka.ADTree (4)
Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999.
9102 runs0 likes3 downloads3 reach56 impact
weka.LADTree (4)
Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.
8855 runs0 likes2 downloads2 reach180 impact
weka.NBTree (3)
Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: Second International Conference on Knoledge Discovery and Data Mining, 202-207, 1996.
7553 runs0 likes2 downloads2 reach7430 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
7534 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC) (2)
Automatically created scikit-learn flow.
6877 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
6765 runs0 likes1 downloads1 reach0 impact
weka.AdaBoostM1_DecisionStump (12)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
6162 runs0 likes2 downloads2 reach244 impact
weka.LogitBoost_DecisionStump (3)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
5944 runs0 likes2 downloads2 reach372 impact
weka.RandomSubSpace_REPTree (4)
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…
5814 runs0 likes2 downloads2 reach172 impact
weka.MultiBoostAB_DecisionStump (3)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
5696 runs0 likes1 downloads1 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (47)
Automatically created scikit-learn flow.
5608 runs0 likes0 downloads0 reach5608 impact
weka.RotationForest_PrincipalComponents_J48 (14)
Juan J. Rodriguez, Ludmila I. Kuncheva, Carlos J. Alonso (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10):1619-1630.…
5531 runs0 likes2 downloads2 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier) (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…
5494 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier) (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 fit…
5142 runs0 likes0 downloads0 reach0 impact
moa.BlastDecayed (1)
Moa implementation of BlastDecayed
5124 runs0 likes2 downloads2 reach1422 impact
weka.Bagging_J48 (2)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
4769 runs0 likes4 downloads4 reach63 impact
mlr.classif.rpart (39)
Learner mlr.classif.rpart from package(s) rpart.
4749 runs0 likes0 downloads0 reach0 impact
weka.KernelLogisticRegression_RBFKernel (1)
Weka implementation of KernelLogisticRegression
4621 runs0 likes2 downloads2 reach52 impact
mlr.classif.glmnet (4)
Learner mlr.classif.glmnet from package(s) glmnet.
4556 runs0 likes0 downloads0 reach1 impact
weka.MultiBoostAB_ADTree (2)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
4295 runs0 likes3 downloads3 reach50 impact
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier)) (1)
Automatically created scikit-learn flow.
4134 runs0 likes0 downloads0 reach0 impact
weka.RealAdaBoost_DecisionStump (2)
J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.
4077 runs0 likes1 downloads1 reach0 impact
weka.IBk (4)
D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
3638 runs0 likes3 downloads3 reach3503 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),logisticregression=sklearn.linear_model.logistic.LogisticRegression) (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…
3550 runs0 likes0 downloads0 reach0 impact
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)) (1)
Automatically created scikit-learn flow.
3540 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputation=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3475 runs0 likes0 downloads0 reach0 impact
weka.AdaBoostM1_J48 (2)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
3395 runs0 likes3 downloads3 reach48 impact
weka.MultilayerPerceptron (4)
Weka implementation of MultilayerPerceptron
3391 runs0 likes8 downloads8 reach3391 impact
weka.Bagging_OneR (2)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
3388 runs0 likes2 downloads2 reach71 impact
mlr.classif.xgboost (4)
Learner mlr.classif.xgboost from package(s) xgboost.
3387 runs0 likes0 downloads0 reach1 impact
weka.J48 (33)
Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
3106 runs0 likes9 downloads9 reach963 impact
weka.Bagging_REPTree (3)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
3098 runs0 likes14 downloads14 reach2965 impact
mlr.regr.rpart (3)
Learner mlr.regr.rpart from package(s) rpart.
2970 runs0 likes0 downloads0 reach2970 impact
mlr.regr.ksvm (2)
Learner mlr.regr.ksvm from package(s) kernlab.
2954 runs0 likes2 downloads2 reach2954 impact
weka.RandomForest (9)
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
2925 runs0 likes10 downloads10 reach683 impact
weka.IBk (7)
D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
2900 runs0 likes6 downloads6 reach2064 impact
mlr.classif.rpart.imputed.dummied.preproc (1)
Learner mlr.classif.rpart.imputed.dummied.preproc from package(s) rpart.
2880 runs0 likes0 downloads0 reach0 impact
weka.Bagging_SMO_PolyKernel (2)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
2878 runs0 likes2 downloads2 reach4 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
2829 runs0 likes0 downloads0 reach0 impact
weka.Bagging_LMT (2)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
2811 runs0 likes2 downloads2 reach65 impact
mlr.classif.J48.imputed.preproc (1)
Learner mlr.classif.J48.imputed.preproc from package(s) RWeka.
2791 runs0 likes0 downloads0 reach0 impact
weka.MultiBoostAB_DecisionStump (2)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
2781 runs0 likes2 downloads2 reach66 impact
mlr.regr.randomForest (2)
Learner mlr.regr.randomForest from package(s) randomForest.
2739 runs0 likes0 downloads0 reach2739 impact
mlr.regr.lm (2)
Learner mlr.regr.lm from package(s) stats.
2712 runs0 likes0 downloads0 reach2712 impact
mlr.regr.pcr (2)
Learner mlr.regr.pcr from package(s) pls.
2708 runs0 likes0 downloads0 reach2708 impact
weka.Bagging_LWL_DecisionStump (2)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
2692 runs0 likes2 downloads2 reach51 impact
mlr.regr.rvm (2)
Learner mlr.regr.rvm from package(s) kernlab.
2684 runs0 likes0 downloads0 reach2684 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (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…
2645 runs0 likes0 downloads0 reach0 impact