train
. Custom models can also be created. See the URL below.Bagged CART (method = 'treebag'
)
For classification and regression using packages
Bagged Flexible Discriminant Analysis (method = 'bagFDA'
)
For classification using packages
degree
)nprune
)
Bagged Logic Regression (method = 'logicBag'
)
For classification and regression using package
nleaves
)ntrees
)
Bagged MARS (method = 'bagEarth'
)
For classification and regression using package
nprune
)degree
)
Bagged Model (method = 'bag'
)
For classification and regression using package
vars
)
Bayesian Generalized Linear Model (method = 'bayesglm'
)
For classification and regression using package
Bayesian Regularized Neural Networks (method = 'brnn'
)
For regression using package
neurons
)
Boosted Classification Trees (method = 'ada'
)
For classification using package
iter
)maxdepth
)nu
)
Boosted Generalized Additive Model (method = 'gamboost'
)
For classification and regression using package
mstop
)prune
)
Boosted Generalized Linear Model (method = 'glmboost'
)
For classification and regression using package
mstop
)prune
)
Boosted Linear Model (method = 'bstLs'
)
For classification and regression using packages
mstop
)nu
)
Boosted Logistic Regression (method = 'LogitBoost'
)
For classification using package
nIter
)
Boosted Smoothing Spline (method = 'bstSm'
)
For classification and regression using packages
mstop
)nu
)
Boosted Tree (method = 'blackboost'
)
For classification and regression using packages
mstop
)maxdepth
)
Boosted Tree (method = 'bstTree'
)
For classification and regression using packages
mstop
)maxdepth
)nu
)
C4.5-like Trees (method = 'J48'
)
For classification using package
C
)
C5.0 (method = 'C5.0'
)
For classification using packages
trials
)model
)winnow
)
CART (method = 'rpart'
)
For classification and regression using package
cp
)
CART (method = 'rpart2'
)
For classification and regression using package
maxdepth
)
Conditional Inference Random Forest (method = 'cforest'
)
For classification and regression using package
mtry
)
Conditional Inference Tree (method = 'ctree'
)
For classification and regression using package
mincriterion
)
Conditional Inference Tree (method = 'ctree2'
)
For classification and regression using package
maxdepth
)
Cost-Sensitive C5.0 (method = 'C5.0Cost'
)
For classification using packages
trials
)model
)winnow
)cost
)
Cost-Sensitive CART (method = 'rpartCost'
)
For classification using package
cp
)Cost
)
Cubist (method = 'cubist'
)
For regression using package
committees
)neighbors
)
Elasticnet (method = 'enet'
)
For regression using package
fraction
)lambda
)
Extreme Learning Machine (method = 'elm'
)
For classification and regression using package
nhid
)actfun
)
Factor-Based Linear Discriminant Analysis (method = 'RFlda'
)
For classification using package
q
)
Flexible Discriminant Analysis (method = 'fda'
)
For classification using packages
degree
)nprune
)
Gaussian Process (method = 'gaussprLinear'
)
For classification and regression using package
Gaussian Process with Polynomial Kernel (method = 'gaussprPoly'
)
For classification and regression using package
degree
)scale
)
Gaussian Process with Radial Basis Function Kernel (method = 'gaussprRadial'
)
For classification and regression using package
sigma
)
Generalized Additive Model using LOESS (method = 'gamLoess'
)
For classification and regression using package
span
)degree
)
Generalized Additive Model using Splines (method = 'gam'
)
For classification and regression using package
select
)method
)
Generalized Additive Model using Splines (method = 'gamSpline'
)
For classification and regression using package
df
)
Generalized Linear Model (method = 'glm'
)
For classification and regression with no tuning parameters
Generalized Linear Model with Stepwise Feature Selection (method = 'glmStepAIC'
)
For classification and regression using package
Generalized Partial Least Squares (method = 'gpls'
)
For classification using package
K.prov
)
glmnet (method = 'glmnet'
)
For classification and regression using package
alpha
)lambda
)
Greedy Prototype Selection (method = 'protoclass'
)
For classification using packages
eps
)Minkowski
)
Heteroscedastic Discriminant Analysis (method = 'hda'
)
For classification using package
gamma
)lambda
)newdim
)
High Dimensional Discriminant Analysis (method = 'hdda'
)
For classification using package
threshold
)model
)
Independent Component Regression (method = 'icr'
)
For regression using package
n.comp
)
k-Nearest Neighbors (method = 'kknn'
)
For classification and regression using package
kmax
)distance
)kernel
)
k-Nearest Neighbors (method = 'knn'
)
For classification and regression with tuning parameters:
k
)
Learning Vector Quantization (method = 'lvq'
)
For classification using package
size
)k
)
Least Angle Regression (method = 'lars'
)
For regression using package
fraction
)
Least Angle Regression (method = 'lars2'
)
For regression using package
step
)
Least Squares Support Vector Machine (method = 'lssvmLinear'
)
For classification using package
Least Squares Support Vector Machine with Polynomial Kernel (method = 'lssvmPoly'
)
For classification using package
degree
)scale
)
Least Squares Support Vector Machine with Radial Basis Function Kernel (method = 'lssvmRadial'
)
For classification using package
sigma
)
Linear Discriminant Analysis (method = 'lda'
)
For classification using package
Linear Discriminant Analysis (method = 'lda2'
)
For classification using package
dimen
)
Linear Discriminant Analysis with Stepwise Feature Selection (method = 'stepLDA'
)
For classification using packages
maxvar
)direction
)
Linear Regression (method = 'lm'
)
For regression with no tuning parameters
Linear Regression with Backwards Selection (method = 'leapBackward'
)
For regression using package
nvmax
)
Linear Regression with Forward Selection (method = 'leapForward'
)
For regression using package
nvmax
)
Linear Regression with Stepwise Selection (method = 'leapSeq'
)
For regression using package
nvmax
)
Linear Regression with Stepwise Selection (method = 'lmStepAIC'
)
For regression using package
Logic Regression (method = 'logreg'
)
For classification and regression using package
treesize
)ntrees
)
Logistic Model Trees (method = 'LMT'
)
For classification using package
iter
)
Maximum Uncertainty Linear Discriminant Analysis (method = 'Mlda'
)
For classification using package
Mixture Discriminant Analysis (method = 'mda'
)
For classification using package
subclasses
)
Model Averaged Neural Network (method = 'avNNet'
)
For classification and regression using package
size
)decay
)bag
)
Model Rules (method = 'M5Rules'
)
For regression using package
pruned
)smoothed
)
Model Tree (method = 'M5'
)
For regression using package
pruned
)smoothed
)rules
)
Multi-Layer Perceptron (method = 'mlp'
)
For classification and regression using package
size
)
Multi-Layer Perceptron (method = 'mlpWeightDecay'
)
For classification and regression using package
size
)decay
)
Multivariate Adaptive Regression Spline (method = 'earth'
)
For classification and regression using package
nprune
)degree
)
Multivariate Adaptive Regression Splines (method = 'gcvEarth'
)
For classification and regression using package
degree
)
Naive Bayes (method = 'nb'
)
For classification using package
fL
)usekernel
)
Nearest Shrunken Centroids (method = 'pam'
)
For classification using package
threshold
)
Neural Network (method = 'neuralnet'
)
For regression using package
layer1
)layer2
)layer3
)
Neural Network (method = 'nnet'
)
For classification and regression using package
size
)decay
)
Neural Networks with Feature Extraction (method = 'pcaNNet'
)
For classification and regression using package
size
)decay
)
Oblique Random Forest (method = 'ORFlog'
)
For classification using package
mtry
)
Oblique Random Forest (method = 'ORFpls'
)
For classification using package
mtry
)
Oblique Random Forest (method = 'ORFridge'
)
For classification using package
mtry
)
Oblique Random Forest (method = 'ORFsvm'
)
For classification using package
mtry
)
Oblique Trees (method = 'oblique.tree'
)
For classification using package
oblique.splits
)variable.selection
)
Parallel Random Forest (method = 'parRF'
)
For classification and regression using package
mtry
)
partDSA (method = 'partDSA'
)
For classification and regression using package
cut.off.growth
)MPD
)
Partial Least Squares (method = 'kernelpls'
)
For classification and regression using package
ncomp
)
Partial Least Squares (method = 'pls'
)
For classification and regression using package
ncomp
)
Partial Least Squares (method = 'simpls'
)
For classification and regression using package
ncomp
)
Partial Least Squares (method = 'widekernelpls'
)
For classification and regression using package
ncomp
)
Penalized Discriminant Analysis (method = 'pda'
)
For classification using package
lambda
)
Penalized Discriminant Analysis (method = 'pda2'
)
For classification using package
df
)
Penalized Linear Discriminant Analysis (method = 'PenalizedLDA'
)
For classification using packages
lambda
)K
)
Penalized Linear Regression (method = 'penalized'
)
For regression using package
lambda1
)lambda2
)
Penalized Logistic Regression (method = 'plr'
)
For classification using package
lambda
)cp
)
Penalized Multinomial Regression (method = 'multinom'
)
For classification using package
decay
)
Polynomial Kernel Regularized Least Squares (method = 'krlsPoly'
)
For regression using package
lambda
)degree
)
Principal Component Analysis (method = 'pcr'
)
For regression using package
ncomp
)
Projection Pursuit Regression (method = 'ppr'
)
For regression with tuning parameters:
nterms
)
Quadratic Discriminant Analysis (method = 'qda'
)
For classification using package
Quadratic Discriminant Analysis with Stepwise Feature Selection (method = 'stepQDA'
)
For classification using packages
maxvar
)direction
)
Quantile Random Forest (method = 'qrf'
)
For regression using package
mtry
)
Quantile Regression Neural Network (method = 'qrnn'
)
For regression using package
n.hidden
)penalty
)bag
)
Radial Basis Function Kernel Regularized Least Squares (method = 'krlsRadial'
)
For regression using packages
lambda
)sigma
)
Radial Basis Function Network (method = 'rbf'
)
For classification using package
size
)
Radial Basis Function Network (method = 'rbfDDA'
)
For classification and regression using package
negativeThreshold
)
Random Ferns (method = 'rFerns'
)
For classification using package
depth
)
Random Forest (method = 'rf'
)
For classification and regression using package
mtry
)
Random Forest by Randomization (method = 'extraTrees'
)
For classification and regression using package
mtry
)numRandomCuts
)
Random Forest with Additional Feature Selection (method = 'Boruta'
)
For classification and regression using packages
mtry
)
Random k-Nearest Neighbors (method = 'rknn'
)
For classification and regression using package
k
)mtry
)
Random k-Nearest Neighbors with Feature Selection (method = 'rknnBel'
)
For classification and regression using packages
k
)mtry
)d
)
Regularized Discriminant Analysis (method = 'rda'
)
For classification using package
gamma
)lambda
)
Regularized Random Forest (method = 'RRF'
)
For classification and regression using packages
mtry
)coefReg
)coefImp
)
Regularized Random Forest (method = 'RRFglobal'
)
For classification and regression using package
mtry
)coefReg
)
Relaxed Lasso (method = 'relaxo'
)
For regression using packages
lambda
)phi
)
Relevance Vector Machines with Linear Kernel (method = 'rvmLinear'
)
For regression using package
Relevance Vector Machines with Polynomial Kernel (method = 'rvmPoly'
)
For regression using package
scale
)degree
)
Relevance Vector Machines with Radial Basis Function Kernel (method = 'rvmRadial'
)
For regression using package
sigma
)
Ridge Regression (method = 'ridge'
)
For regression using package
lambda
)
Ridge Regression with Variable Selection (method = 'foba'
)
For regression using package
k
)lambda
)
Robust Linear Discriminant Analysis (method = 'Linda'
)
For classification using package
Robust Linear Model (method = 'rlm'
)
For regression using package
Robust Quadratic Discriminant Analysis (method = 'QdaCov'
)
For classification using package
Robust Regularized Linear Discriminant Analysis (method = 'rrlda'
)
For classification using package
lambda
)hp
)penalty
)
Robust SIMCA (method = 'RSimca'
)
For classification using package
ROC-Based Classifier (method = 'rocc'
)
For classification using package
xgenes
)
Rule-Based Classifier (method = 'JRip'
)
For classification using package
NumOpt
)
Rule-Based Classifier (method = 'PART'
)
For classification using package
threshold
)pruned
)
Self-Organizing Map (method = 'bdk'
)
For classification and regression using package
xdim
)ydim
)xweight
)topo
)
Self-Organizing Maps (method = 'xyf'
)
For classification and regression using package
xdim
)ydim
)xweight
)topo
)
Shrinkage Discriminant Analysis (method = 'sda'
)
For classification using package
diagonal
)lambda
)
SIMCA (method = 'CSimca'
)
For classification using package
Single C5.0 Ruleset (method = 'C5.0Rules'
)
For classification using package
Single C5.0 Tree (method = 'C5.0Tree'
)
For classification using package
Single Rule Classification (method = 'OneR'
)
For classification using package
Sparse Linear Discriminant Analysis (method = 'sparseLDA'
)
For classification using package
NumVars
)lambda
)
Sparse Mixture Discriminant Analysis (method = 'smda'
)
For classification using package
NumVars
)lambda
)R
)
Sparse Partial Least Squares (method = 'spls'
)
For classification and regression using package
K
)eta
)kappa
)
Stabilized Linear Discriminant Analysis (method = 'slda'
)
For classification using package
Stacked AutoEncoder Deep Neural Network (method = 'dnn'
)
For classification and regression using package
layer1
)layer2
)layer3
)hidden_dropout
)visible_dropout
)
Stepwise Diagonal Linear Discriminant Analysis (method = 'sddaLDA'
)
For classification using package
Stepwise Diagonal Quadratic Discriminant Analysis (method = 'sddaQDA'
)
For classification using package
Stochastic Gradient Boosting (method = 'gbm'
)
For classification and regression using packages
n.trees
)interaction.depth
)shrinkage
)
Supervised Principal Component Analysis (method = 'superpc'
)
For regression using package
threshold
)n.components
)
Support Vector Machines with Class Weights (method = 'svmRadialWeights'
)
For classification using package
sigma
)C
)Weight
)
Support Vector Machines with Linear Kernel (method = 'svmLinear'
)
For classification and regression using package
C
)
Support Vector Machines with Polynomial Kernel (method = 'svmPoly'
)
For classification and regression using package
degree
)scale
)C
)
Support Vector Machines with Radial Basis Function Kernel (method = 'svmRadial'
)
For classification and regression using package
sigma
)C
)
Support Vector Machines with Radial Basis Function Kernel (method = 'svmRadialCost'
)
For classification and regression using package
C
)
The lasso (method = 'lasso'
)
For regression using package
fraction
)
Tree Models from Genetic Algorithms (method = 'evtree'
)
For classification and regression using package
alpha
)
Tree-Based Ensembles (method = 'nodeHarvest'
)
For classification and regression using package
maxinter
)mode
)
Variational Bayesian Multinomial Probit Regression (method = 'vbmpRadial'
)
For classification using package
estimateTheta
)train
'' (