train
. Custom models can also be created. See the URL below.AdaBoost.M1 (method = 'AdaBoost.M1'
)
For classification using packages
mfinal
, numeric)maxdepth
, numeric)coeflearn
, character)
Adaptive Mixture Discriminant Analysis (method = 'amdai'
)
For classification using package
model
, character)
Adaptive-Network-Based Fuzzy Inference System (method = 'ANFIS'
)
For regression using package
num.labels
, numeric)max.iter
, numeric)
Bagged AdaBoost (method = 'AdaBag'
)
For classification using packages
mfinal
, numeric)maxdepth
, numeric)
Bagged CART (method = 'treebag'
)
For classification and regression using packages
Bagged FDA using gCV Pruning (method = 'bagFDAGCV'
)
For classification using package
degree
, numeric)
Bagged Flexible Discriminant Analysis (method = 'bagFDA'
)
For classification using packages
degree
, numeric)nprune
, numeric)
Bagged Logic Regression (method = 'logicBag'
)
For classification and regression using package
nleaves
, numeric)ntrees
, numeric)
Bagged MARS (method = 'bagEarth'
)
For classification and regression using package
nprune
, numeric)degree
, numeric)
Bagged MARS using gCV Pruning (method = 'bagEarthGCV'
)
For classification and regression using package
degree
, numeric)
Bagged Model (method = 'bag'
)
For classification and regression using package
vars
, numeric)
Bayesian Additive Regression Trees (method = 'bartMachine'
)
For classification and regression using package
num_trees
, numeric)k
, numeric)alpha
, numeric)beta
, numeric)nu
, numeric)
Bayesian Generalized Linear Model (method = 'bayesglm'
)
For classification and regression using package
Bayesian Regularized Neural Networks (method = 'brnn'
)
For regression using package
neurons
, numeric)
Bayesian Ridge Regression (method = 'bridge'
)
For regression using package
Bayesian Ridge Regression (Model Averaged) (method = 'blassoAveraged'
)
For regression using package
Binary Discriminant Analysis (method = 'binda'
)
For classification using package
lambda.freqs
, numeric)
Boosted Classification Trees (method = 'ada'
)
For classification using packages
iter
, numeric)maxdepth
, numeric)nu
, numeric)
Boosted Generalized Additive Model (method = 'gamboost'
)
For classification and regression using package
mstop
, numeric)prune
, character)
Boosted Generalized Linear Model (method = 'glmboost'
)
For classification and regression using package
mstop
, numeric)prune
, character)
Boosted Linear Model (method = 'BstLm'
)
For classification and regression using packages
mstop
, numeric)nu
, numeric)
Boosted Logistic Regression (method = 'LogitBoost'
)
For classification using package
nIter
, numeric)
Boosted Smoothing Spline (method = 'bstSm'
)
For classification and regression using packages
mstop
, numeric)nu
, numeric)
Boosted Tree (method = 'blackboost'
)
For classification and regression using packages
mstop
, numeric)maxdepth
, numeric)
Boosted Tree (method = 'bstTree'
)
For classification and regression using packages
mstop
, numeric)maxdepth
, numeric)nu
, numeric)
C4.5-like Trees (method = 'J48'
)
For classification using package
C
, numeric)
C5.0 (method = 'C5.0'
)
For classification using packages
trials
, numeric)model
, character)winnow
, logical)
CART (method = 'rpart'
)
For classification and regression using package
cp
, numeric)
CART (method = 'rpart2'
)
For classification and regression using package
maxdepth
, numeric)
CHi-squared Automated Interaction Detection (method = 'chaid'
)
For classification using package
alpha2
, numeric)alpha3
, numeric)alpha4
, numeric)
Conditional Inference Random Forest (method = 'cforest'
)
For classification and regression using package
mtry
, numeric)
Conditional Inference Tree (method = 'ctree'
)
For classification and regression using package
mincriterion
, numeric)
Conditional Inference Tree (method = 'ctree2'
)
For classification and regression using package
maxdepth
, numeric)
Cost-Sensitive C5.0 (method = 'C5.0Cost'
)
For classification using packages
trials
, numeric)model
, character)winnow
, logical)cost
, numeric)
Cost-Sensitive CART (method = 'rpartCost'
)
For classification using package
cp
, numeric)Cost
, numeric)
Cubist (method = 'cubist'
)
For regression using package
committees
, numeric)neighbors
, numeric)
Distance Weighted Discrimination with Polynomial Kernel (method = 'dwdPoly'
)
For classification using package
lambda
, numeric)qval
, numeric)degree
, numeric)scale
, numeric)
Distance Weighted Discrimination with Radial Basis Function Kernel (method = 'dwdRadial'
)
For classification using packages
lambda
, numeric)qval
, numeric)sigma
, numeric)
Dynamic Evolving Neural-Fuzzy Inference System (method = 'DENFIS'
)
For regression using package
Dthr
, numeric)max.iter
, numeric)
Elasticnet (method = 'enet'
)
For regression using package
fraction
, numeric)lambda
, numeric)
Ensemble Partial Least Squares Regression (method = 'enpls'
)
For regression using package
maxcomp
, numeric)
Ensemble Partial Least Squares Regression with Feature Selection (method = 'enpls.fs'
)
For regression using package
maxcomp
, numeric)threshold
, numeric)
Ensembles of Generalized Lienar Models (method = 'randomGLM'
)
For classification and regression using package
maxInteractionOrder
, numeric)
eXtreme Gradient Boosting (method = 'xgbLinear'
)
For classification and regression using package
nrounds
, numeric)lambda
, numeric)alpha
, numeric)
eXtreme Gradient Boosting (method = 'xgbTree'
)
For classification and regression using packages
nrounds
, numeric)max_depth
, numeric)eta
, numeric)gamma
, numeric)
Extreme Learning Machine (method = 'elm'
)
For classification and regression using package
nhid
, numeric)actfun
, character)
Factor-Based Linear Discriminant Analysis (method = 'RFlda'
)
For classification using package
q
, numeric)
Flexible Discriminant Analysis (method = 'fda'
)
For classification using packages
degree
, numeric)nprune
, numeric)
Fuzzy Inference Rules by Descent Method (method = 'FIR.DM'
)
For regression using package
num.labels
, numeric)max.iter
, numeric)
Fuzzy Rules Using Chi's Method (method = 'FRBCS.CHI'
)
For classification using package
num.labels
, numeric)type.mf
, character)
Fuzzy Rules Using Genetic Cooperative-Competitive Learning (method = 'GFS.GCCL'
)
For classification using package
num.labels
, numeric)popu.size
, numeric)max.gen
, numeric)
Fuzzy Rules Using Genetic Cooperative-Competitive Learning and Pittsburgh (method = 'FH.GBML'
)
For classification using package
max.num.rule
, numeric)popu.size
, numeric)max.gen
, numeric)
Fuzzy Rules Using the Structural Learning Algorithm on Vague Environment (method = 'SLAVE'
)
For classification using package
num.labels
, numeric)max.iter
, numeric)max.gen
, numeric)
Fuzzy Rules via MOGUL (method = 'GFS.FR.MOGUL'
)
For regression using package
max.gen
, numeric)max.iter
, numeric)max.tune
, numeric)
Fuzzy Rules via Thrift (method = 'GFS.THRIFT'
)
For regression using package
popu.size
, numeric)num.labels
, numeric)max.gen
, numeric)
Fuzzy Rules with Weight Factor (method = 'FRBCS.W'
)
For classification using package
num.labels
, numeric)type.mf
, character)
Gaussian Process (method = 'gaussprLinear'
)
For classification and regression using package
Gaussian Process with Polynomial Kernel (method = 'gaussprPoly'
)
For classification and regression using package
degree
, numeric)scale
, numeric)
Gaussian Process with Radial Basis Function Kernel (method = 'gaussprRadial'
)
For classification and regression using package
sigma
, numeric)
Generalized Additive Model using LOESS (method = 'gamLoess'
)
For classification and regression using package
span
, numeric)degree
, numeric)
Generalized Additive Model using Splines (method = 'gam'
)
For classification and regression using package
select
, logical)method
, character)
Generalized Additive Model using Splines (method = 'gamSpline'
)
For classification and regression using package
df
, numeric)
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
, numeric)
Genetic Lateral Tuning and Rule Selection of Linguistic Fuzzy Systems (method = 'GFS.LT.RS'
)
For regression using package
popu.size
, numeric)num.labels
, numeric)max.gen
, numeric)
glmnet (method = 'glmnet'
)
For classification and regression using package
alpha
, numeric)lambda
, numeric)
Greedy Prototype Selection (method = 'protoclass'
)
For classification using packages
eps
, numeric)Minkowski
, numeric)
Heteroscedastic Discriminant Analysis (method = 'hda'
)
For classification using package
gamma
, numeric)lambda
, numeric)newdim
, numeric)
High Dimensional Discriminant Analysis (method = 'hdda'
)
For classification using package
threshold
, character)model
, numeric)
Hybrid Neural Fuzzy Inference System (method = 'HYFIS'
)
For regression using package
num.labels
, numeric)max.iter
, numeric)
Independent Component Regression (method = 'icr'
)
For regression using package
n.comp
, numeric)
k-Nearest Neighbors (method = 'kknn'
)
For classification and regression using package
kmax
, numeric)distance
, numeric)kernel
, character)
k-Nearest Neighbors (method = 'knn'
)
For classification and regression with tuning parameters:
k
, numeric)
Knn regression via sklearn.neighbors.KNeighborsRegressor (method = 'pythonKnnReg'
)
For regression using package
n_neighbors
, numeric)weights
, character)algorithm
, character)leaf_size
, numeric)metric
, character)p
, numeric)
Learning Vector Quantization (method = 'lvq'
)
For classification using package
size
, numeric)k
, numeric)
Least Angle Regression (method = 'lars'
)
For regression using package
fraction
, numeric)
Least Angle Regression (method = 'lars2'
)
For regression using package
step
, numeric)
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
, numeric)scale
, numeric)
Least Squares Support Vector Machine with Radial Basis Function Kernel (method = 'lssvmRadial'
)
For classification using package
sigma
, numeric)
Linear Discriminant Analysis (method = 'lda'
)
For classification using package
Linear Discriminant Analysis (method = 'lda2'
)
For classification using package
dimen
, numeric)
Linear Discriminant Analysis with Stepwise Feature Selection (method = 'stepLDA'
)
For classification using packages
maxvar
, numeric)direction
, character)
Linear Distance Weighted Discrimination (method = 'dwdLinear'
)
For classification using package
lambda
, numeric)qval
, numeric)
Linear Regression (method = 'lm'
)
For regression with no tuning parameters
Linear Regression with Backwards Selection (method = 'leapBackward'
)
For regression using package
nvmax
, numeric)
Linear Regression with Forward Selection (method = 'leapForward'
)
For regression using package
nvmax
, numeric)
Linear Regression with Stepwise Selection (method = 'leapSeq'
)
For regression using package
nvmax
, numeric)
Linear Regression with Stepwise Selection (method = 'lmStepAIC'
)
For regression using package
Localized Linear Discriminant Analysis (method = 'loclda'
)
For classification using package
k
, numeric)
Logic Regression (method = 'logreg'
)
For classification and regression using package
treesize
, numeric)ntrees
, numeric)
Logistic Model Trees (method = 'LMT'
)
For classification using package
iter
, numeric)
Maximum Uncertainty Linear Discriminant Analysis (method = 'Mlda'
)
For classification using package
Mixture Discriminant Analysis (method = 'mda'
)
For classification using package
subclasses
, numeric)
Model Averaged Neural Network (method = 'avNNet'
)
For classification and regression using package
size
, numeric)decay
, numeric)bag
, logical)
Model Rules (method = 'M5Rules'
)
For regression using package
pruned
, character)smoothed
, character)
Model Tree (method = 'M5'
)
For regression using package
pruned
, character)smoothed
, character)rules
, character)
Multi-Layer Perceptron (method = 'mlp'
)
For classification and regression using package
size
, numeric)
Multi-Layer Perceptron (method = 'mlpWeightDecay'
)
For classification and regression using package
size
, numeric)decay
, numeric)
Multivariate Adaptive Regression Spline (method = 'earth'
)
For classification and regression using package
nprune
, numeric)degree
, numeric)
Multivariate Adaptive Regression Splines (method = 'gcvEarth'
)
For classification and regression using package
degree
, numeric)
Naive Bayes (method = 'nb'
)
For classification using package
fL
, numeric)usekernel
, logical)
Naive Bayes Classifier (method = 'nbDiscrete'
)
For classification using package
smooth
, numeric)
Naive Bayes Classifier with Attribute Weighting (method = 'awnb'
)
For classification using package
smooth
, numeric)
Nearest Shrunken Centroids (method = 'pam'
)
For classification using package
threshold
, numeric)
Neural Network (method = 'neuralnet'
)
For regression using package
layer1
, numeric)layer2
, numeric)layer3
, numeric)
Neural Network (method = 'nnet'
)
For classification and regression using package
size
, numeric)decay
, numeric)
Neural Networks with Feature Extraction (method = 'pcaNNet'
)
For classification and regression using package
size
, numeric)decay
, numeric)
Non-Convex Penalized Quantile Regression (method = 'rqnc'
)
For regression using package
lambda
, numeric)penalty
, character)
Non-Negative Least Squares (method = 'nnls'
)
For regression using package
Oblique Random Forest (method = 'ORFlog'
)
For classification using package
mtry
, numeric)
Oblique Random Forest (method = 'ORFpls'
)
For classification using package
mtry
, numeric)
Oblique Random Forest (method = 'ORFridge'
)
For classification using package
mtry
, numeric)
Oblique Random Forest (method = 'ORFsvm'
)
For classification using package
mtry
, numeric)
Oblique Trees (method = 'oblique.tree'
)
For classification using package
oblique.splits
, character)variable.selection
, character)
Optimal Weighted Nearest Neighbor Classifier (method = 'ownn'
)
For classification using package
K
, numeric)
Ordered Logistic or Probit Regression (method = 'polr'
)
For classification using package
Parallel Random Forest (method = 'parRF'
)
For classification and regression using packages
mtry
, numeric)
partDSA (method = 'partDSA'
)
For classification and regression using package
cut.off.growth
, numeric)MPD
, numeric)
Partial Least Squares (method = 'kernelpls'
)
For classification and regression using package
ncomp
, numeric)
Partial Least Squares (method = 'pls'
)
For classification and regression using package
ncomp
, numeric)
Partial Least Squares (method = 'simpls'
)
For classification and regression using package
ncomp
, numeric)
Partial Least Squares (method = 'widekernelpls'
)
For classification and regression using package
ncomp
, numeric)
Partial Least Squares Generalized Linear Models (method = 'plsRglm'
)
For classification and regression using package
nt
, numeric)alpha.pvals.expli
, numeric)
Penalized Discriminant Analysis (method = 'pda'
)
For classification using package
lambda
, numeric)
Penalized Discriminant Analysis (method = 'pda2'
)
For classification using package
df
, numeric)
Penalized Linear Discriminant Analysis (method = 'PenalizedLDA'
)
For classification using packages
lambda
, numeric)K
, numeric)
Penalized Linear Regression (method = 'penalized'
)
For regression using package
lambda1
, numeric)lambda2
, numeric)
Penalized Logistic Regression (method = 'plr'
)
For classification using package
lambda
, numeric)cp
, character)
Penalized Multinomial Regression (method = 'multinom'
)
For classification using package
decay
, numeric)
Polynomial Kernel Regularized Least Squares (method = 'krlsPoly'
)
For regression using package
lambda
, numeric)degree
, numeric)
Principal Component Analysis (method = 'pcr'
)
For regression using package
ncomp
, numeric)
Projection Pursuit Regression (method = 'ppr'
)
For regression with tuning parameters:
nterms
, numeric)
Quadratic Discriminant Analysis (method = 'qda'
)
For classification using package
Quadratic Discriminant Analysis with Stepwise Feature Selection (method = 'stepQDA'
)
For classification using packages
maxvar
, numeric)direction
, character)
Quantile Random Forest (method = 'qrf'
)
For regression using package
mtry
, numeric)
Quantile Regression Neural Network (method = 'qrnn'
)
For regression using package
n.hidden
, numeric)penalty
, numeric)bag
, logical)
Quantile Regression with LASSO penalty (method = 'rqlasso'
)
For regression using package
lambda
, numeric)
Radial Basis Function Kernel Regularized Least Squares (method = 'krlsRadial'
)
For regression using packages
lambda
, numeric)sigma
, numeric)
Radial Basis Function Network (method = 'rbf'
)
For classification and regression using package
size
, numeric)
Radial Basis Function Network (method = 'rbfDDA'
)
For classification and regression using package
negativeThreshold
, numeric)
Random Ferns (method = 'rFerns'
)
For classification using package
depth
, numeric)
Random Forest (method = 'ranger'
)
For classification and regression using packages
mtry
, numeric)
Random Forest (method = 'rf'
)
For classification and regression using package
mtry
, numeric)
Random Forest by Randomization (method = 'extraTrees'
)
For classification and regression using package
mtry
, numeric)numRandomCuts
, numeric)
Random Forest Rule-Based Model (method = 'rfRules'
)
For classification and regression using packages
mtry
, numeric)maxdepth
, numeric)
Random Forest with Additional Feature Selection (method = 'Boruta'
)
For classification and regression using packages
mtry
, numeric)
Random k-Nearest Neighbors (method = 'rknn'
)
For classification and regression using package
k
, numeric)mtry
, numeric)
Random k-Nearest Neighbors with Feature Selection (method = 'rknnBel'
)
For classification and regression using packages
k
, numeric)mtry
, numeric)d
, numeric)
Regularized Discriminant Analysis (method = 'rda'
)
For classification using package
gamma
, numeric)lambda
, numeric)
Regularized Random Forest (method = 'RRF'
)
For classification and regression using packages
mtry
, numeric)coefReg
, numeric)coefImp
, numeric)
Regularized Random Forest (method = 'RRFglobal'
)
For classification and regression using package
mtry
, numeric)coefReg
, numeric)
Relaxed Lasso (method = 'relaxo'
)
For regression using packages
lambda
, numeric)phi
, numeric)
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
, numeric)degree
, numeric)
Relevance Vector Machines with Radial Basis Function Kernel (method = 'rvmRadial'
)
For regression using package
sigma
, numeric)
Ridge Regression (method = 'ridge'
)
For regression using package
lambda
, numeric)
Ridge Regression with Variable Selection (method = 'foba'
)
For regression using package
k
, numeric)lambda
, numeric)
Robust Linear Discriminant Analysis (method = 'Linda'
)
For classification using package
Robust Linear Model (method = 'rlm'
)
For regression using package
Robust Mixture Discriminant Analysis (method = 'rmda'
)
For classification using package
K
, numeric)model
, character)
Robust Quadratic Discriminant Analysis (method = 'QdaCov'
)
For classification using package
Robust Regularized Linear Discriminant Analysis (method = 'rrlda'
)
For classification using package
lambda
, numeric)hp
, numeric)penalty
, character)
Robust SIMCA (method = 'RSimca'
)
For classification using package
ROC-Based Classifier (method = 'rocc'
)
For classification using package
xgenes
, numeric)
Rotation Forest (method = 'rotationForest'
)
For classification using package
K
, numeric)L
, numeric)
Rotation Forest (method = 'rotationForestCp'
)
For classification using packages
K
, numeric)L
, numeric)cp
, numeric)
Rule-Based Classifier (method = 'JRip'
)
For classification using package
NumOpt
, numeric)
Rule-Based Classifier (method = 'PART'
)
For classification using package
threshold
, numeric)pruned
, character)
Self-Organizing Map (method = 'bdk'
)
For classification and regression using package
xdim
, numeric)ydim
, numeric)xweight
, numeric)topo
, character)
Self-Organizing Maps (method = 'xyf'
)
For classification and regression using package
xdim
, numeric)ydim
, numeric)xweight
, numeric)topo
, character)
Semi-Naive Structure Learner Wrapper (method = 'nbSearch'
)
For classification using package
k
, numeric)epsilon
, numeric)smooth
, numeric)final_smooth
, numeric)direction
, character)
Shrinkage Discriminant Analysis (method = 'sda'
)
For classification using package
diagonal
, logical)lambda
, numeric)
SIMCA (method = 'CSimca'
)
For classification using package
Simplified TSK Fuzzy Rules (method = 'FS.HGD'
)
For regression using package
num.labels
, numeric)max.iter
, numeric)
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 Distance Weighted Discrimination (method = 'sdwd'
)
For classification using package
lambda
, numeric)lambda2
, numeric)
Sparse Linear Discriminant Analysis (method = 'sparseLDA'
)
For classification using package
NumVars
, numeric)lambda
, numeric)
Sparse Mixture Discriminant Analysis (method = 'smda'
)
For classification using package
NumVars
, numeric)lambda
, numeric)R
, numeric)
Sparse Partial Least Squares (method = 'spls'
)
For classification and regression using package
K
, numeric)eta
, numeric)kappa
, numeric)
Spike and Slab Regression (method = 'spikeslab'
)
For regression using packages
vars
, numeric)
Stabilized Linear Discriminant Analysis (method = 'slda'
)
For classification using package
Stabilized Nearest Neighbor Classifier (method = 'snn'
)
For classification using package
lambda
, numeric)
Stacked AutoEncoder Deep Neural Network (method = 'dnn'
)
For classification and regression using package
layer1
, numeric)layer2
, numeric)layer3
, numeric)hidden_dropout
, numeric)visible_dropout
, numeric)
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
, numeric)interaction.depth
, numeric)shrinkage
, numeric)n.minobsinnode
, numeric)
Subtractive Clustering and Fuzzy c-Means Rules (method = 'SBC'
)
For regression using package
r.a
, numeric)eps.high
, numeric)eps.low
, numeric)
Supervised Principal Component Analysis (method = 'superpc'
)
For regression using package
threshold
, numeric)n.components
, numeric)
Support Vector Machines with Boundrange String Kernel (method = 'svmBoundrangeString'
)
For classification and regression using package
length
, numeric)C
, numeric)
Support Vector Machines with Class Weights (method = 'svmRadialWeights'
)
For classification using package
sigma
, numeric)C
, numeric)Weight
, numeric)
Support Vector Machines with Exponential String Kernel (method = 'svmExpoString'
)
For classification and regression using package
lambda
, numeric)C
, numeric)
Support Vector Machines with Linear Kernel (method = 'svmLinear'
)
For classification and regression using package
C
, numeric)
Support Vector Machines with Linear Kernel (method = 'svmLinear2'
)
For classification and regression using package
cost
, numeric)
Support Vector Machines with Polynomial Kernel (method = 'svmPoly'
)
For classification and regression using package
degree
, numeric)scale
, numeric)C
, numeric)
Support Vector Machines with Radial Basis Function Kernel (method = 'svmRadial'
)
For classification and regression using package
sigma
, numeric)C
, numeric)
Support Vector Machines with Radial Basis Function Kernel (method = 'svmRadialCost'
)
For classification and regression using package
C
, numeric)
Support Vector Machines with Spectrum String Kernel (method = 'svmSpectrumString'
)
For classification and regression using package
length
, numeric)C
, numeric)
The Bayesian lasso (method = 'blasso'
)
For regression using package
sparsity
, numeric)
The lasso (method = 'lasso'
)
For regression using package
fraction
, numeric)
Tree Augmented Naive Bayes Classifier (method = 'tan'
)
For classification using package
score
, character)smooth
, numeric)
Tree Augmented Naive Bayes Classifier Structure Learner Wrapper (method = 'tanSearch'
)
For classification using package
k
, numeric)epsilon
, numeric)smooth
, numeric)final_smooth
, numeric)sp
, logical)
Tree Augmented Naive Bayes Classifier with Attribute Weighting (method = 'awtan'
)
For classification using package
score
, character)smooth
, numeric)
Tree Models from Genetic Algorithms (method = 'evtree'
)
For classification and regression using package
alpha
, numeric)
Tree-Based Ensembles (method = 'nodeHarvest'
)
For classification and regression using package
maxinter
, numeric)mode
, character)
Variational Bayesian Multinomial Probit Regression (method = 'vbmpRadial'
)
For classification using package
estimateTheta
, character)
Wang and Mendel Fuzzy Rules (method = 'WM'
)
For regression using package
num.labels
, numeric)type.mf
, character)
Weighted Subspace Random Forest (method = 'wsrf'
)
For classification using package
mtry
, numeric)train
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