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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,kernelpca=sklearn.decomposition.kernel_pca.KernelPCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
2 runs0 likes0 downloads0 reach0 impact
weka.NaiveBayes (16)
George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
22 runs0 likes0 downloads0 reach18 impact
weka.IBk (11)
D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
306 runs0 likes0 downloads0 reach304 impact
weka.SimpleLogistic (6)
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European Conference on Principles and…
16 runs0 likes0 downloads0 reach15 impact
weka.MultilayerPerceptron (8)
Weka implementation of MultilayerPerceptron
291 runs0 likes0 downloads0 reach287 impact
weka.Bagging_J48 (9)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
8 runs0 likes0 downloads0 reach7 impact
weka.Bagging_RandomForest (12)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
13 runs0 likes0 downloads0 reach11 impact
weka.RandomForest (12)
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
391 runs1 likes0 downloads1 reach390 impact
weka.MultiSearch_J48 (5)
Weka implementation of MultiSearch
2 runs0 likes0 downloads0 reach2 impact
weka.SimpleLinearRegression (6)
Weka implementation of SimpleLinearRegression
0 runs0 likes0 downloads0 reach0 impact
weka.SMO_PolyKernel (15)
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,…
335 runs0 likes1 downloads1 reach330 impact
weka.PolyKernel (11)
Weka implementation of PolyKernel
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (32)
Learner mlr.classif.rpart from package(s) rpart.
10 runs0 likes0 downloads0 reach0 impact
mlr.classif.cforest (3)
Learner mlr.classif.cforest from package(s) party.
10 runs0 likes0 downloads0 reach0 impact
mlr.classif.randomForestSRC (2)
Learner mlr.classif.randomForestSRC from package(s) randomForestSRC.
10 runs0 likes0 downloads0 reach0 impact
weka.Bagging_MultilayerPerceptron (4)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
2 runs0 likes0 downloads0 reach0 impact
weka.Bagging_SimpleLogistic (2)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
1 runs0 likes0 downloads0 reach0 impact
weka.LinearRegression (9)
Weka implementation of LinearRegression
0 runs0 likes0 downloads0 reach0 impact
weka.Logistic (8)
le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.
10 runs0 likes0 downloads0 reach9 impact
weka.Bagging_JRip (5)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
1 runs0 likes0 downloads0 reach0 impact
weka.JRip (7)
William W. Cohen: Fast Effective Rule Induction. In: Twelfth International Conference on Machine Learning, 115-123, 1995.
16 runs0 likes0 downloads0 reach15 impact
weka.ClassificationViaRegression_M5P (5)
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
1 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_ZeroR (3)
Weka implementation of CostSensitiveClassifier
5 runs0 likes0 downloads0 reach3 impact
weka.CostSensitiveClassifier_NaiveBayes (4)
Weka implementation of CostSensitiveClassifier
13 runs0 likes0 downloads0 reach11 impact
weka.AutoWEKAClassifier (2)
Weka implementation of AutoWEKAClassifier
1 runs1 likes1 downloads2 reach1 impact
weka.OneR (20)
R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
4 runs0 likes0 downloads0 reach3 impact
weka.HoeffdingTree (6)
Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 97-106, 2001.
30 runs0 likes0 downloads0 reach28 impact
weka.GaussianProcesses_PolyKernel (11)
David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
0 runs0 likes0 downloads0 reach0 impact
weka.RandomCommittee_RandomForest (1)
Weka implementation of RandomCommittee
4 runs0 likes0 downloads0 reach2 impact
weka.RandomCommittee_M5P (1)
Weka implementation of RandomCommittee
0 runs0 likes0 downloads0 reach0 impact
weka.RandomCommittee_RandomTree (7)
Weka implementation of RandomCommittee
6 runs0 likes0 downloads0 reach3 impact
weka.RandomTree (14)
Weka implementation of RandomTree
4 runs0 likes0 downloads0 reach4 impact
weka.Stacking_ZeroR (3)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
2 runs0 likes0 downloads0 reach1 impact
weka.CostSensitiveClassifier_SMO_PolyKernel (2)
Weka implementation of CostSensitiveClassifier
4 runs0 likes0 downloads0 reach3 impact
weka.Bagging_REPTree (26)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
4 runs0 likes0 downloads0 reach3 impact
weka.REPTree (18)
Weka implementation of REPTree
6 runs0 likes0 downloads0 reach6 impact
weka.SMO_NormalizedPolyKernel (2)
J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning,…
4 runs0 likes0 downloads0 reach3 impact
weka.NormalizedPolyKernel (7)
Weka implementation of NormalizedPolyKernel
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
weka.IsolationForest (4)
Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou: Isolation Forest. In: ICDM, 413-422, 2008.
0 runs0 likes0 downloads0 reach0 impact
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.
0 runs0 likes0 downloads0 reach0 impact
weka.Bagging_Stacking_ZeroR (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
1 runs0 likes0 downloads0 reach0 impact
weka.Bagging_SMO_RBFKernel (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
1 runs0 likes0 downloads0 reach0 impact
weka.AdaBoostM1_RandomForest (6)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
8 runs0 likes0 downloads0 reach5 impact
weka.Stacking_SMO (1)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
1 runs0 likes0 downloads0 reach0 impact
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,…
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.xgboost (5)
Learner mlr.classif.xgboost from package(s) xgboost.
819 runs0 likes0 downloads0 reach814 impact
mlr.classif.ranger (8)
Learner mlr.classif.ranger from package(s) ranger.
693 runs0 likes0 downloads0 reach692 impact
mlr.classif.ranger (9)
Learner mlr.classif.ranger from package(s) ranger.
573666 runs0 likes3 downloads3 reach1 impact
weka.OneR (21)
R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
2 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_SimpleLogistic (2)
Weka implementation of CostSensitiveClassifier
30 runs0 likes0 downloads0 reach28 impact
mlr.classif.ranger (10)
Learner mlr.classif.ranger from package(s) ranger.
1891 runs0 likes0 downloads0 reach0 impact
mlr.classif.svm (8)
Learner mlr.classif.svm from package(s) e1071.
2543 runs0 likes0 downloads0 reach0 impact
mlr.classif.glmnet (5)
Learner mlr.classif.glmnet from package(s) glmnet.
106003 runs0 likes3 downloads3 reach1 impact
mlr.classif.xgboost (6)
Learner mlr.classif.xgboost from package(s) xgboost.
75581 runs0 likes1 downloads1 reach1 impact
mlr.classif.kknn (11)
Learner mlr.classif.kknn from package(s) !kknn.
2419 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_LinearRegression (3)
Weka implementation of CostSensitiveClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_Logistic (3)
Weka implementation of CostSensitiveClassifier
11 runs0 likes0 downloads0 reach11 impact
weka.CostSensitiveClassifier_Vote_NaiveBayes (1)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
weka.HNB (8)
H. Zhang, L. Jiang, J. Su: Hidden Naive Bayes. In: Twentieth National Conference on Artificial Intelligence, 919-924, 2005.
0 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_J48 (2)
Weka implementation of AttributeSelectedClassifier
387 runs0 likes0 downloads0 reach386 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
34 runs0 likes0 downloads0 reach0 impact
helper.dual_imputer.DualImputer (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
28 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
64 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,neuralnetwork=helper.nn.NeuralNetwork) (1)
Automatically created scikit-learn flow.
14 runs0 likes0 downloads0 reach0 impact
helper.nn.NeuralNetwork (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
22 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_SMO_PolyKernel (2)
Weka implementation of AttributeSelectedClassifier
266 runs0 likes0 downloads0 reach264 impact
weka.BayesNet (2)
Weka implementation of BayesNet
12 runs0 likes0 downloads0 reach11 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,standardscaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
2 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
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…
0 runs0 likes0 downloads0 reach0 impact
mlr.classif.glmnet (6)
Learner mlr.classif.glmnet from package(s) glmnet.
496 runs0 likes0 downloads0 reach0 impact
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.
0 runs0 likes0 downloads0 reach0 impact
weka.PART (6)
Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
4 runs0 likes0 downloads0 reach3 impact
mlr.classif.xgboost (7)
Learner mlr.classif.xgboost from package(s) xgboost.
13577 runs0 likes1 downloads1 reach0 impact
weka.MultiSearch_RandomForest (3)
Weka implementation of MultiSearch
1 runs0 likes0 downloads0 reach0 impact
weka.MultiSearch_Logistic (2)
Weka implementation of MultiSearch
1 runs0 likes0 downloads0 reach0 impact
weka.RandomForest (13)
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
4 runs0 likes0 downloads0 reach3 impact
weka.AttributeSelectedClassifier_RandomForest (2)
Weka implementation of AttributeSelectedClassifier
336 runs0 likes0 downloads0 reach334 impact
weka.InputMappedClassifier_ZeroR (3)
Weka implementation of InputMappedClassifier
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
8 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
2 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_Stacking_SMO (1)
Weka implementation of AttributeSelectedClassifier
0 runs0 likes0 downloads0 reach0 impact
weka.Stacking_AttributeSelectedClassifier (1)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
2 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier (1)
Weka implementation of AttributeSelectedClassifier
12 runs0 likes0 downloads0 reach12 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,robustscaler=sklearn.preprocessing.data.RobustScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
2 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,robustscaler=sklearn.preprocessing.data.RobustScaler,lr=__main__.LR) (1)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach0 impact