Integrated Learners

This page lists the learning methods already integrated in mlr.

Columns Num., Fac., Ord., NAs, and Weights indicate if a method can cope with numerical, factor, and ordered factor predictors, if it can deal with missing values in a meaningful way (other than simply removing observations with missing values) and if observation weights are supported.

Column Props shows further properties of the learning methods specific to the type of learning task. See also RLearner() for details.

Classification (75)

For classification the following additional learner properties are relevant and shown in column Props:

• prob: The method can predict probabilities,
• oneclass, twoclass, multiclass: One-class, two-class (binary) or multi-class classification problems be handled,
• class.weights: Class weights can be handled.

Class / Short Name / Name	Packages	Num.	Fac.	Ord.	NAs	Weights	Props	Note
classif.ada ada ada Boosting	ada rpart	X	X				prob twoclass	xval has been set to 0 by default for speed.
classif.adaboostm1 adaboostm1 ada Boosting M1	RWeka	X	X				prob twoclass multiclass	NAs are directly passed to WEKA with na.action = na.pass.
classif.binomial binomial Binomial Regression	stats	X	X			X	prob twoclass	Delegates to glm with freely choosable binomial link function via learner parameter link. We set ‘model’ to FALSE by default to save memory.
classif.boosting adabag Adabag Boosting	adabag rpart	X	X		X		prob twoclass multiclass featimp	xval has been set to 0 by default for speed.
classif.bst bst Gradient Boosting	bst rpart	X					twoclass	Renamed parameter learner to Learner due to nameclash with setHyperPars. Default changes: Learner = "ls", xval = 0, and maxdepth = 1.
classif.C50 C50 C50	C50	X	X		X	X	prob twoclass multiclass
classif.cforest cforest Random forest based on conditional inference trees	party	X	X	X	X	X	prob twoclass multiclass featimp	See ?ctree_control for possible breakage for nominal features with missingness.
classif.clusterSVM clusterSVM Clustered Support Vector Machines	SwarmSVM LiblineaR	X					twoclass	centers set to 2 by default.
classif.ctree ctree Conditional Inference Trees	party	X	X	X	X	X	prob twoclass multiclass	See ?ctree_control for possible breakage for nominal features with missingness.
classif.cvglmnet cvglmnet GLM with Lasso or Elasticnet Regularization (Cross Validated Lambda)	glmnet	X	X			X	prob twoclass multiclass	The family parameter is set to binomial for two-class problems and to multinomial otherwise. Factors automatically get converted to dummy columns, ordered factors to integer. glmnet uses a global control object for its parameters. mlr resets all control parameters to their defaults before setting the specified parameters and after training. If you are setting glmnet.control parameters through glmnet.control, you need to save and re-set them after running the glmnet learner.
classif.dbnDNN dbn.dnn Deep neural network with weights initialized by DBN	deepnet	X					prob twoclass multiclass	output set to "softmax" by default.
classif.dcSVM dcSVM Divided-Conquer Support Vector Machines	SwarmSVM e1071	X					twoclass
classif.earth fda Flexible Discriminant Analysis	earth stats	X	X			X	prob twoclass multiclass	This learner performs flexible discriminant analysis using the earth algorithm. na.action is set to na.fail and only this is supported.
classif.evtree evtree Evolutionary learning of globally optimal trees	evtree	X	X	X		X	prob twoclass multiclass	pmutatemajor, pmutateminor, pcrossover, psplit, and pprune, are scaled internally to sum to 100.
classif.fdausc.glm fdausc.glm Generalized Linear Models classification on FDA	fda.usc						prob twoclass multiclass functionals	model$C[[1]] is set to quote(classif.glm)
classif.fdausc.kernel fdausc.kernel Kernel classification on FDA	fda.usc						prob twoclass multiclass single.functional	Argument draw=FALSE is used as default.
classif.fdausc.knn fdausc.knn fdausc.knn	fda.usc					X	prob twoclass multiclass single.functional	Argument draw=FALSE is used as default.
classif.fdausc.np fdausc.np Nonparametric classification on FDA	fda.usc						prob twoclass multiclass single.functional	Argument draw=FALSE is used as default. Additionally, mod$C[[1]] is set to quote(classif.np)
classif.FDboost FDboost Functional linear array classification boosting	FDboost mboost	X					prob twoclass functionals	Uses only one base learner per functional or scalar covariate. Uses the same hyperparameters for every baselearner. Currently does not support interaction between scalar covariates. Default for family has been set to ‘Binomial’, as ‘Gaussian’ is not applicable.
classif.featureless featureless Featureless classifier	mlr	X	X	X	X		prob twoclass multiclass functionals
classif.fgam FGAM functional general additive model	refund						prob twoclass functionals single.functional
classif.fnn fnn Fast k-Nearest Neighbour	FNN	X					twoclass multiclass
classif.gamboost gamboost Gradient boosting with smooth components	mboost	X	X			X	prob twoclass	family has been set to Binomial() by default. For ‘family’ ‘AUC’ and ‘AdaExp’ probabilities cannot be predicted.
classif.gaterSVM gaterSVM Mixture of SVMs with Neural Network Gater Function	SwarmSVM	X					twoclass	m set to 3 and max.iter set to 1 by default.
classif.gausspr gausspr Gaussian Processes	kernlab	X	X				prob twoclass multiclass	Kernel parameters have to be passed directly and not by using the kpar list in gausspr. Note that fit has been set to FALSE by default for speed.
classif.gbm gbm Gradient Boosting Machine	gbm	X	X		X	X	prob twoclass multiclass featimp	keep.data is set to FALSE to reduce memory requirements. Param ‘n.cores’ has been to set to ‘1’ by default to suppress parallelization by the package.
classif.glmboost glmboost Boosting for GLMs	mboost	X	X			X	prob twoclass	family has been set to Binomial by default. For ‘family’ ‘AUC’ and ‘AdaExp’ probabilities cannot be predcited.
classif.glmnet glmnet GLM with Lasso or Elasticnet Regularization	glmnet	X	X			X	prob twoclass multiclass	The family parameter is set to binomial for two-class problems and to multinomial otherwise. Factors automatically get converted to dummy columns, ordered factors to integer. Parameter s (value of the regularization parameter used for predictions) is set to 0.01 by default, but needs to be tuned by the user. glmnet uses a global control object for its parameters. mlr resets all control parameters to their defaults before setting the specified parameters and after training. If you are setting glmnet.control parameters through glmnet.control, you need to save and re-set them after running the glmnet learner.
classif.h2o.deeplearning h2o.dl h2o.deeplearning	h2o	X	X		X	X	prob twoclass multiclass featimp	The default value of missing_values_handling is "MeanImputation", so missing values are automatically mean-imputed.
classif.h2o.gbm h2o.gbm h2o.gbm	h2o	X	X		X		prob twoclass multiclass featimp	‘distribution’ is set automatically to ‘gaussian’.
classif.h2o.glm h2o.glm h2o.glm	h2o	X	X		X	X	prob twoclass featimp	family is always set to "binomial" to get a binary classifier. The default value of missing_values_handling is "MeanImputation", so missing values are automatically mean-imputed.
classif.h2o.randomForest h2o.rf h2o.randomForest	h2o	X	X		X		prob twoclass multiclass featimp
classif.IBk ibk k-Nearest Neighbours	RWeka	X	X				prob twoclass multiclass
classif.J48 j48 J48 Decision Trees	RWeka	X	X		X		prob twoclass multiclass	NAs are directly passed to WEKA with na.action = na.pass.
classif.JRip jrip Propositional Rule Learner	RWeka	X	X		X		prob twoclass multiclass	NAs are directly passed to WEKA with na.action = na.pass.
classif.kknn kknn k-Nearest Neighbor	kknn	X	X				prob twoclass multiclass
classif.knn knn k-Nearest Neighbor	class	X					twoclass multiclass
classif.ksvm ksvm Support Vector Machines	kernlab	X	X				prob twoclass multiclass class.weights	Kernel parameters have to be passed directly and not by using the kpar list in ksvm. Note that fit has been set to FALSE by default for speed.
classif.lda lda Linear Discriminant Analysis	MASS	X	X				prob twoclass multiclass	Learner parameter predict.method maps to method in predict.lda.
classif.LiblineaRL1L2SVC liblinl1l2svc L1-Regularized L2-Loss Support Vector Classification	LiblineaR	X					twoclass multiclass class.weights
classif.LiblineaRL1LogReg liblinl1logreg L1-Regularized Logistic Regression	LiblineaR	X					prob twoclass multiclass class.weights
classif.LiblineaRL2L1SVC liblinl2l1svc L2-Regularized L1-Loss Support Vector Classification	LiblineaR	X					twoclass multiclass class.weights
classif.LiblineaRL2LogReg liblinl2logreg L2-Regularized Logistic Regression	LiblineaR	X					prob twoclass multiclass class.weights	type = 0 (the default) is primal and type = 7 is dual problem.
classif.LiblineaRL2SVC liblinl2svc L2-Regularized L2-Loss Support Vector Classification	LiblineaR	X					twoclass multiclass class.weights	type = 2 (the default) is primal and type = 1 is dual problem.
classif.LiblineaRMultiClassSVC liblinmulticlasssvc Support Vector Classification by Crammer and Singer	LiblineaR	X					twoclass multiclass class.weights
classif.logreg logreg Logistic Regression	stats	X	X			X	prob twoclass	Delegates to glm with family = binomial(link = 'logit'). We set ‘model’ to FALSE by default to save memory.
classif.lssvm lssvm Least Squares Support Vector Machine	kernlab	X	X				twoclass multiclass	fitted has been set to FALSE by default for speed.
classif.lvq1 lvq1 Learning Vector Quantization	class	X					twoclass multiclass
classif.mda mda Mixture Discriminant Analysis	mda	X	X				prob twoclass multiclass	keep.fitted has been set to FALSE by default for speed and we use start.method = "lvq" for more robust behavior / less technical crashes.
classif.mlp mlp Multi-Layer Perceptron	RSNNS	X					prob twoclass multiclass
classif.multinom multinom Multinomial Regression	nnet	X	X			X	prob twoclass multiclass
classif.naiveBayes nbayes Naive Bayes	e1071	X	X		X		prob twoclass multiclass
classif.neuralnet neuralnet Neural Network from neuralnet	neuralnet	X					prob twoclass	err.fct has been set to ce and linear.output to FALSE to do classification.
classif.nnet nnet Neural Network	nnet	X	X			X	prob twoclass multiclass	linout=TRUE is hardcoded for regression. size has been set to 3 by default.
classif.nnTrain nn.train Training Neural Network by Backpropagation	deepnet	X					prob twoclass multiclass	output set to softmax by default. max.number.of.layers can be set to control and tune the maximal number of layers specified via hidden.
classif.OneR oner 1-R Classifier	RWeka	X	X		X		prob twoclass multiclass	NAs are directly passed to WEKA with na.action = na.pass.
classif.pamr pamr Nearest shrunken centroid	pamr	X					prob twoclass	Threshold for prediction (threshold.predict) has been set to 1 by default.
classif.PART part PART Decision Lists	RWeka	X	X		X		prob twoclass multiclass	NAs are directly passed to WEKA with na.action = na.pass.
classif.penalized penalized Penalized Logistic Regression	penalized	X	X	X			prob twoclass	trace=FALSE was set by default to disable logging output.
classif.plr plr Logistic Regression with a L2 Penalty	stepPlr	X	X			X	prob twoclass	AIC and BIC penalty types can be selected via the new parameter cp.type.
classif.plsdaCaret plsdacaret Partial Least Squares (PLS) Discriminant Analysis	caret pls	X					prob twoclass multiclass
classif.probit probit Probit Regression	stats	X	X			X	prob twoclass	Delegates to glm with family = binomial(link = 'probit'). We set ‘model’ to FALSE by default to save memory.
classif.qda qda Quadratic Discriminant Analysis	MASS	X	X				prob twoclass multiclass	Learner parameter predict.method maps to method in predict.qda.
classif.randomForest rf Random Forest	randomForest	X	X	X			prob twoclass multiclass class.weights featimp oobpreds	Note that the rf can freeze the R process if trained on a task with 1 feature which is constant. This can happen in feature forward selection, also due to resampling, and you need to remove such features with removeConstantFeatures.
classif.ranger ranger Random Forests	ranger	X	X	X		X	prob twoclass multiclass featimp oobpreds	By default, internal parallelization is switched off (num.threads = 1), verbose output is disabled, respect.unordered.factors is set to order for all splitrules. If predict.type=‘prob’ we set ‘probability=TRUE’ in ranger.
classif.rda rda Regularized Discriminant Analysis	klaR	X	X				prob twoclass multiclass	estimate.error has been set to FALSE by default for speed.
classif.rFerns rFerns Random ferns	rFerns	X	X	X			twoclass multiclass oobpreds
classif.rotationForest rotationForest Rotation Forest	rotationForest	X	X	X			prob twoclass
classif.rpart rpart Decision Tree	rpart	X	X	X	X	X	prob twoclass multiclass featimp	xval has been set to 0 by default for speed.
classif.RRF RRF Regularized Random Forests	RRF	X	X				prob twoclass multiclass featimp
classif.saeDNN sae.dnn Deep neural network with weights initialized by Stacked AutoEncoder	deepnet	X					prob twoclass multiclass	output set to "softmax" by default.
classif.sda sda Shrinkage Discriminant Analysis	sda	X					prob twoclass multiclass
classif.sparseLDA sparseLDA Sparse Discriminant Analysis	sparseLDA MASS elasticnet	X					prob twoclass multiclass	Arguments Q and stop are not yet provided as they depend on the task.
classif.svm svm Support Vector Machines (libsvm)	e1071	X	X				prob twoclass multiclass class.weights
classif.xgboost xgboost eXtreme Gradient Boosting	xgboost	X			X	X	prob twoclass multiclass featimp	All settings are passed directly, rather than through xgboost’s params argument. nrounds has been set to 1 and verbose to 0 by default. num_class is set internally, so do not set this manually.

Regression (54)

Additional learner properties:

• se: Standard errors can be predicted.

Class / Short Name / Name	Packages	Num.	Fac.	Ord.	NAs	Weights	Props	Note
regr.bcart bcart Bayesian CART	tgp	X	X				se
regr.bgp bgp Bayesian Gaussian Process	tgp	X					se
regr.bgpllm bgpllm Bayesian Gaussian Process with jumps to the Limiting Linear Model	tgp	X					se
regr.blm blm Bayesian Linear Model	tgp	X					se
regr.brnn brnn Bayesian regularization for feed-forward neural networks	brnn	X	X
regr.bst bst Gradient Boosting	bst rpart	X						Renamed parameter learner to Learner due to nameclash with setHyperPars. Default changes: Learner = "ls", xval = 0, and maxdepth = 1.
regr.btgp btgp Bayesian Treed Gaussian Process	tgp	X	X				se
regr.btgpllm btgpllm Bayesian Treed Gaussian Process with jumps to the Limiting Linear Model	tgp	X	X				se
regr.btlm btlm Bayesian Treed Linear Model	tgp	X	X				se
regr.cforest cforest Random Forest Based on Conditional Inference Trees	party	X	X	X	X	X	featimp	See ?ctree_control for possible breakage for nominal features with missingness.
regr.crs crs Regression Splines	crs	X	X			X	se
regr.ctree ctree Conditional Inference Trees	party	X	X	X	X	X		See ?ctree_control for possible breakage for nominal features with missingness.
regr.cubist cubist Cubist	Cubist	X	X		X
regr.cvglmnet cvglmnet GLM with Lasso or Elasticnet Regularization (Cross Validated Lambda)	glmnet	X	X			X		Factors automatically get converted to dummy columns, ordered factors to integer. glmnet uses a global control object for its parameters. mlr resets all control parameters to their defaults before setting the specified parameters and after training. If you are setting glmnet.control parameters through glmnet.control, you need to save and re-set them after running the glmnet learner.
regr.earth earth Multivariate Adaptive Regression Splines	earth	X	X
regr.evtree evtree Evolutionary learning of globally optimal trees	evtree	X	X	X		X		pmutatemajor, pmutateminor, pcrossover, psplit, and pprune, are scaled internally to sum to 100.
regr.FDboost FDboost Functional linear array regression boosting	FDboost mboost	X					functionals	Only allow one base learner for functional covariate and one base learner for scalar covariate, the parameters for these base learners are the same. Also we currently do not support interaction between scalar covariates
regr.featureless featureless Featureless regression	mlr	X	X	X	X		functionals
regr.fgam FGAM functional general additive model	refund						functionals single.functional
regr.fnn fnn Fast k-Nearest Neighbor	FNN	X
regr.frbs frbs Fuzzy Rule-based Systems	frbs	X
regr.gamboost gamboost Gradient Boosting with Smooth Components	mboost	X	X			X
regr.gausspr gausspr Gaussian Processes	kernlab	X	X				se	Kernel parameters have to be passed directly and not by using the kpar list in gausspr. Note that fit has been set to FALSE by default for speed.
regr.gbm gbm Gradient Boosting Machine	gbm	X	X		X	X	featimp	keep.data is set to FALSE to reduce memory requirements, distribution has been set to "gaussian" by default.Param ‘n.cores’ has been to set to ‘1’ by default to suppress parallelization by the package.
regr.glm glm Generalized Linear Regression	stats	X	X			X	se	‘family’ must be a character and every family has its own link, i.e. family = ‘gaussian’, link.gaussian = ‘identity’, which is also the default. We set ‘model’ to FALSE by default to save memory.
regr.glmboost glmboost Boosting for GLMs	mboost	X	X			X
regr.glmnet glmnet GLM with Lasso or Elasticnet Regularization	glmnet	X	X	X		X		Factors automatically get converted to dummy columns, ordered factors to integer. Parameter s (value of the regularization parameter used for predictions) is set to 0.01 by default, but needs to be tuned by the user. glmnet uses a global control object for its parameters. mlr resets all control parameters to their defaults before setting the specified parameters and after training. If you are setting glmnet.control parameters through glmnet.control, you need to save and re-set them after running the glmnet learner.
regr.GPfit GPfit Gaussian Process	GPfit	X					se	(1) As the optimization routine assumes that the inputs are scaled to the unit hypercube [0,1]^d, the input gets scaled for each variable by default. If this is not wanted, scale = FALSE has to be set. (2) We replace the GPfit parameter ‘corr = list(type = ’exponential’,power = 1.95)’ to be seperate parameters ‘type’ and ‘power’, in the case of corr = list(type = ‘matern’, nu = 0.5), the seperate parameters are ‘type’ and ‘matern_nu_k = 0’, and nu is computed by ‘nu = (2 * matern_nu_k + 1) / 2 = 0.5’
regr.h2o.deeplearning h2o.dl h2o.deeplearning	h2o	X	X		X	X		The default value of missing_values_handling is "MeanImputation", so missing values are automatically mean-imputed.
regr.h2o.gbm h2o.gbm h2o.gbm	h2o	X	X		X
regr.h2o.glm h2o.glm h2o.glm	h2o	X	X		X	X		family is always set to "gaussian". The default value of missing_values_handling is "MeanImputation", so missing values are automatically mean-imputed.
regr.h2o.randomForest h2o.rf h2o.randomForest	h2o	X	X		X
regr.IBk ibk K-Nearest Neighbours	RWeka	X	X
regr.kknn kknn K-Nearest-Neighbor regression	kknn	X	X
regr.km km Kriging	DiceKriging	X					se	In predict, we currently always use type = "SK". The extra parameter jitter (default is FALSE) enables adding a very small jitter (order 1e-12) to the x-values before prediction, as predict.km reproduces the exact y-values of the training data points, when you pass them in, even if the nugget effect is turned on. We further introduced nugget.stability which sets the nugget to nugget.stability * var(y) before each training to improve numerical stability. We recommend a setting of 10^-8
regr.ksvm ksvm Support Vector Machines	kernlab	X	X					Kernel parameters have to be passed directly and not by using the kpar list in ksvm. Note that fit has been set to FALSE by default for speed.
regr.laGP laGP Local Approximate Gaussian Process	laGP	X					se
regr.LiblineaRL2L1SVR liblinl2l1svr L2-Regularized L1-Loss Support Vector Regression	LiblineaR	X						Parameter svr_eps has been set to 0.1 by default.
regr.LiblineaRL2L2SVR liblinl2l2svr L2-Regularized L2-Loss Support Vector Regression	LiblineaR	X						type = 11 (the default) is primal and type = 12 is dual problem. Parameter svr_eps has been set to 0.1 by default.
regr.lm lm Simple Linear Regression	stats	X	X			X	se
regr.mars mars Multivariate Adaptive Regression Splines	mda	X
regr.mob mob Model-based Recursive Partitioning Yielding a Tree with Fitted Models Associated with each Terminal Node	party modeltools	X	X			X
regr.nnet nnet Neural Network	nnet	X	X			X		size has been set to 3 by default.
regr.pcr pcr Principal Component Regression	pls	X	X
regr.penalized penalized Penalized Regression	penalized	X	X					trace=FALSE was set by default to disable logging output.
regr.plsr plsr Partial Least Squares Regression	pls	X	X
regr.randomForest rf Random Forest	randomForest	X	X	X			featimp oobpreds se	See the section about ‘regr.randomForest’ in ?makeLearner for information about se estimation. Note that the rf can freeze the R process if trained on a task with 1 feature which is constant. This can happen in feature forward selection, also due to resampling, and you need to remove such features with removeConstantFeatures. keep.inbag is NULL by default but if predict.type = ‘se’ and se.method = ‘jackknife’ (the default) then it is automatically set to TRUE.
regr.ranger ranger Random Forests	ranger	X	X	X		X	featimp oobpreds se	By default, internal parallelization is switched off (num.threads = 1), verbose output is disabled, respect.unordered.factors is set to order for all splitrules. All settings are changeable. mtry.perc sets mtry to mtry.perc*getTaskNFeats(.task). Default for mtry is the floor of square root of number of features in task. SE estimation is mc bias-corrected jackknife after bootstrap, see the section about ‘regr.randomForest’ in ?makeLearner for more details.
regr.rpart rpart Decision Tree	rpart	X	X	X	X	X	featimp	xval has been set to 0 by default for speed.
regr.RRF RRF Regularized Random Forests	RRF	X	X	X			featimp
regr.rsm rsm Response Surface Regression	rsm	X						You select the order of the regression by using modelfun = "FO" (first order), "TWI" (two-way interactions, this is with 1st oder terms!) and "SO" (full second order).
regr.rvm rvm Relevance Vector Machine	kernlab	X	X					Kernel parameters have to be passed directly and not by using the kpar list in rvm. Note that fit has been set to FALSE by default for speed.
regr.svm svm Support Vector Machines (libsvm)	e1071	X	X
regr.xgboost xgboost eXtreme Gradient Boosting	xgboost	X			X	X	featimp	All settings are passed directly, rather than through xgboost’s params argument. nrounds has been set to 1 and verbose to 0 by default.

Survival analysis (9)

Additional learner properties:

• prob: Probabilities can be predicted,
• rcens, lcens, icens: The learner can handle right, left and/or interval censored data.

Class / Short Name / Name	Packages	Num.	Fac.	Ord.	NAs	Weights	Props	Note
surv.cforest crf Random Forest based on Conditional Inference Trees	party survival	X	X	X	X	X	featimp	See ?ctree_control for possible breakage for nominal features with missingness.
surv.coxph coxph Cox Proportional Hazard Model	survival	X	X			X
surv.cvglmnet cvglmnet GLM with Regularization (Cross Validated Lambda)	glmnet	X	X	X		X		Factors automatically get converted to dummy columns, ordered factors to integer.
surv.gamboost gamboost Gradient boosting with smooth components	survival mboost	X	X	X		X		family has been set to CoxPH() by default.
surv.gbm gbm Gradient Boosting Machine	gbm	X	X		X	X	featimp	keep.data is set to FALSE to reduce memory requirements.
surv.glmboost glmboost Gradient Boosting with Componentwise Linear Models	survival mboost	X	X	X		X		family has been set to CoxPH() by default.
surv.glmnet glmnet GLM with Regularization	glmnet	X	X	X		X		Factors automatically get converted to dummy columns, ordered factors to integer.Parameter s (value of the regularization parameter used for predictions) is set to 0.1 by default, but needs to be tuned by the user. glmnet uses a global control object for its parameters. mlr resets all control parameters to their defaults before setting the specified parametersand after training. If you are setting glmnet.control parameters through glmnet.control,you need to save and re-set them after running the glmnet learner.
surv.ranger ranger Random Forests	ranger	X	X	X		X	featimp	By default, internal parallelization is switched off (num.threads = 1), verbose output is disabled, respect.unordered.factors is set to order for all splitrules. All settings are changeable.
surv.rpart rpart Survival Tree	rpart	X	X	X	X	X	featimp	xval has been set to 0 by default for speed.

Cluster analysis (10)

Additional learner properties:

• prob: Probabilities can be predicted.

Class / Short Name / Name	Packages	Num.	Fac.	Ord.	NAs	Weights	Props	Note
cluster.cmeans cmeans Fuzzy C-Means Clustering	e1071 clue	X					prob	The predict method uses cl_predict from the clue package to compute the cluster memberships for new data. The default centers = 2 is added so the method runs without setting parameters, but this must in reality of course be changed by the user.
cluster.Cobweb cobweb Cobweb Clustering Algorithm	RWeka	X
cluster.dbscan dbscan DBScan Clustering	fpc	X						A cluster index of NA indicates noise points. Specify method = 'dist' if the data should be interpreted as dissimilarity matrix or object. Otherwise Euclidean distances will be used.
cluster.EM em Expectation-Maximization Clustering	RWeka	X
cluster.FarthestFirst farthestfirst FarthestFirst Clustering Algorithm	RWeka	X
cluster.kkmeans kkmeans Kernel K-Means	kernlab	X						centers has been set to 2L by default. The nearest center in kernel distance determines cluster assignment of new data points. Kernel parameters have to be passed directly and not by using the kpar list in kkmeans
cluster.kmeans kmeans K-Means	stats clue	X					prob	The predict method uses cl_predict from the clue package to compute the cluster memberships for new data. The default centers = 2 is added so the method runs without setting parameters, but this must in reality of course be changed by the user.
cluster.MiniBatchKmeans MBatchKmeans MiniBatchKmeans	ClusterR	X					prob	Calls MiniBatchKmeans of package ClusterR. Argument clusters has default value of 2 if not provided by user.
cluster.SimpleKMeans simplekmeans K-Means Clustering	RWeka	X
cluster.XMeans xmeans XMeans (k-means with automatic determination of k)	RWeka	X						You may have to install the XMeans Weka package: WPM('install-package', 'XMeans').

Cost-sensitive classification

For ordinary misclassification costs you can use all the standard classification methods listed above.

For example-dependent costs there are several ways to generate cost-sensitive learners from ordinary regression and classification learners. See section cost-sensitive classification and the documentation of makeCostSensClassifWrapper(), makeCostSensRegrWrapper() and makeCostSensWeightedPairsWrapper() for details.

Multilabel classification (2)

Class / Short Name / Name	Packages	Num.	Fac.	Ord.	NAs	Weights	Props	Note
multilabel.cforest cforest Random forest based on conditional inference trees	party	X	X	X	X	X	prob
multilabel.rFerns rFerns Random ferns	rFerns	X	X	X

Moreover, you can use the binary relevance method to apply ordinary classification learners to the multilabel problem. See the documentation of function makeMultilabelBinaryRelevanceWrapper() and the tutorial section on multilabel classification for details.