train_model_list {caret} | R Documentation

## A List of Available Models in train

### Description

These models are included in the package via wrappers for `train`. Custom models can also be created. See the URL below.

AdaBoost Classification Trees (`method = 'adaboost'`)

For classification using package fastAdaboost with tuning parameters:

- Number of Trees (`nIter`, numeric)

- Method (`method`, character)

AdaBoost.M1 (`method = 'AdaBoost.M1'`)

For classification using packages adabag and plyr with tuning parameters:

- Number of Trees (`mfinal`, numeric)

- Max Tree Depth (`maxdepth`, numeric)

- Coefficient Type (`coeflearn`, character)

Adaptive Mixture Discriminant Analysis (`method = 'amdai'`)

For classification using package adaptDA with tuning parameters:

- Model Type (`model`, character)

Adaptive-Network-Based Fuzzy Inference System (`method = 'ANFIS'`)

For regression using package frbs with tuning parameters:

- Number of Fuzzy Terms (`num.labels`, numeric)

- Max. Iterations (`max.iter`, numeric)

Adjacent Categories Probability Model for Ordinal Data (`method = 'vglmAdjCat'`)

For classification using package VGAM with tuning parameters:

- Parallel Curves (`parallel`, logical)

- Link Function (`link`, character)

Bagged AdaBoost (`method = 'AdaBag'`)

For classification using packages adabag and plyr with tuning parameters:

- Number of Trees (`mfinal`, numeric)

- Max Tree Depth (`maxdepth`, numeric)

Bagged CART (`method = 'treebag'`)

For classification and regression using packages ipred, plyr and e1071 with no tuning parameters

Bagged FDA using gCV Pruning (`method = 'bagFDAGCV'`)

For classification using package earth with tuning parameters:

- Product Degree (`degree`, numeric)

Bagged Flexible Discriminant Analysis (`method = 'bagFDA'`)

For classification using packages earth and mda with tuning parameters:

- Product Degree (`degree`, numeric)

- Number of Terms (`nprune`, numeric)

Bagged Logic Regression (`method = 'logicBag'`)

For classification and regression using package logicFS with tuning parameters:

- Maximum Number of Leaves (`nleaves`, numeric)

- Number of Trees (`ntrees`, numeric)

Bagged MARS (`method = 'bagEarth'`)

For classification and regression using package earth with tuning parameters:

- Number of Terms (`nprune`, numeric)

- Product Degree (`degree`, numeric)

Bagged MARS using gCV Pruning (`method = 'bagEarthGCV'`)

For classification and regression using package earth with tuning parameters:

- Product Degree (`degree`, numeric)

Bagged Model (`method = 'bag'`)

For classification and regression using package caret with tuning parameters:

- Number of Randomly Selected Predictors (`vars`, numeric)

Bayesian Additive Regression Trees (`method = 'bartMachine'`)

For classification and regression using package bartMachine with tuning parameters:

- Number of Trees (`num_trees`, numeric)

- Prior Boundary (`k`, numeric)

- Base Terminal Node Hyperparameter (`alpha`, numeric)

- Power Terminal Node Hyperparameter (`beta`, numeric)

- Degrees of Freedom (`nu`, numeric)

Bayesian Generalized Linear Model (`method = 'bayesglm'`)

For classification and regression using package arm with no tuning parameters

Bayesian Regularized Neural Networks (`method = 'brnn'`)

For regression using package brnn with tuning parameters:

- Number of Neurons (`neurons`, numeric)

Bayesian Ridge Regression (`method = 'bridge'`)

For regression using package monomvn with no tuning parameters

Bayesian Ridge Regression (Model Averaged) (`method = 'blassoAveraged'`)

For regression using package monomvn with no tuning parameters

Binary Discriminant Analysis (`method = 'binda'`)

For classification using package binda with tuning parameters:

- Shrinkage Intensity (`lambda.freqs`, numeric)

Boosted Classification Trees (`method = 'ada'`)

For classification using packages ada and plyr with tuning parameters:

- Number of Trees (`iter`, numeric)

- Max Tree Depth (`maxdepth`, numeric)

- Learning Rate (`nu`, numeric)

Boosted Generalized Additive Model (`method = 'gamboost'`)

For classification and regression using packages mboost and plyr with tuning parameters:

- Number of Boosting Iterations (`mstop`, numeric)

- AIC Prune? (`prune`, character)

Boosted Generalized Linear Model (`method = 'glmboost'`)

For classification and regression using packages plyr and mboost with tuning parameters:

- Number of Boosting Iterations (`mstop`, numeric)

- AIC Prune? (`prune`, character)

Boosted Linear Model (`method = 'BstLm'`)

For classification and regression using packages bst and plyr with tuning parameters:

- Number of Boosting Iterations (`mstop`, numeric)

- Shrinkage (`nu`, numeric)

Boosted Logistic Regression (`method = 'LogitBoost'`)

For classification using package caTools with tuning parameters:

- Number of Boosting Iterations (`nIter`, numeric)

Boosted Smoothing Spline (`method = 'bstSm'`)

For classification and regression using packages bst and plyr with tuning parameters:

- Number of Boosting Iterations (`mstop`, numeric)

- Shrinkage (`nu`, numeric)

Boosted Tree (`method = 'blackboost'`)

For classification and regression using packages party, mboost and plyr with tuning parameters:

- Number of Trees (`mstop`, numeric)

- Max Tree Depth (`maxdepth`, numeric)

Boosted Tree (`method = 'bstTree'`)

For classification and regression using packages bst and plyr with tuning parameters:

- Number of Boosting Iterations (`mstop`, numeric)

- Max Tree Depth (`maxdepth`, numeric)

- Shrinkage (`nu`, numeric)

C4.5-like Trees (`method = 'J48'`)

For classification using package RWeka with tuning parameters:

- Confidence Threshold (`C`, numeric)

C5.0 (`method = 'C5.0'`)

For classification using packages C50 and plyr with tuning parameters:

- Number of Boosting Iterations (`trials`, numeric)

- Model Type (`model`, character)

- Winnow (`winnow`, logical)

CART (`method = 'rpart'`)

For classification and regression using package rpart with tuning parameters:

- Complexity Parameter (`cp`, numeric)

CART (`method = 'rpart1SE'`)

For classification and regression using package rpart with no tuning parameters

CART (`method = 'rpart2'`)

For classification and regression using package rpart with tuning parameters:

- Max Tree Depth (`maxdepth`, numeric)

CART or Ordinal Responses (`method = 'rpartScore'`)

For classification using packages rpartScore and plyr with tuning parameters:

- Complexity Parameter (`cp`, numeric)

- Split Function (`split`, character)

- Pruning Measure (`prune`, character)

CHi-squared Automated Interaction Detection (`method = 'chaid'`)

For classification using package CHAID with tuning parameters:

- Merging Threshold (`alpha2`, numeric)

- Splitting former Merged Threshold (`alpha3`, numeric)

- Splitting former Merged Threshold (`alpha4`, numeric)

Conditional Inference Random Forest (`method = 'cforest'`)

For classification and regression using package party with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

Conditional Inference Tree (`method = 'ctree'`)

For classification and regression using package party with tuning parameters:

- 1 - P-Value Threshold (`mincriterion`, numeric)

Conditional Inference Tree (`method = 'ctree2'`)

For classification and regression using package party with tuning parameters:

- Max Tree Depth (`maxdepth`, numeric)

- 1 - P-Value Threshold (`mincriterion`, numeric)

Continuation Ratio Model for Ordinal Data (`method = 'vglmContRatio'`)

For classification using package VGAM with tuning parameters:

- Parallel Curves (`parallel`, logical)

- Link Function (`link`, character)

Cost-Sensitive C5.0 (`method = 'C5.0Cost'`)

For classification using packages C50 and plyr with tuning parameters:

- Number of Boosting Iterations (`trials`, numeric)

- Model Type (`model`, character)

- Winnow (`winnow`, logical)

- Cost (`cost`, numeric)

Cost-Sensitive CART (`method = 'rpartCost'`)

For classification using package rpart with tuning parameters:

- Complexity Parameter (`cp`, numeric)

- Cost (`Cost`, numeric)

Cubist (`method = 'cubist'`)

For regression using package Cubist with tuning parameters:

- Number of Committees (`committees`, numeric)

- Number of Instances (`neighbors`, numeric)

Cumulative Probability Model for Ordinal Data (`method = 'vglmCumulative'`)

For classification using package VGAM with tuning parameters:

- Parallel Curves (`parallel`, logical)

- Link Function (`link`, character)

DeepBoost (`method = 'deepboost'`)

For classification using package deepboost with tuning parameters:

- Number of Boosting Iterations (`num_iter`, numeric)

- Tree Depth (`tree_depth`, numeric)

- L1 Regularization (`beta`, numeric)

- Tree Depth Regularization (`lambda`, numeric)

- Loss (`loss_type`, character)

Diagonal Discriminant Analysis (`method = 'dda'`)

For classification using package sparsediscrim with tuning parameters:

- Model (`model`, character)

- Shrinkage Type (`shrinkage`, character)

Distance Weighted Discrimination with Polynomial Kernel (`method = 'dwdPoly'`)

For classification using package kerndwd with tuning parameters:

- Regularization Parameter (`lambda`, numeric)

- q (`qval`, numeric)

- Polynomial Degree (`degree`, numeric)

- Scale (`scale`, numeric)

Distance Weighted Discrimination with Radial Basis Function Kernel (`method = 'dwdRadial'`)

For classification using packages kernlab and kerndwd with tuning parameters:

- Regularization Parameter (`lambda`, numeric)

- q (`qval`, numeric)

- Sigma (`sigma`, numeric)

Dynamic Evolving Neural-Fuzzy Inference System (`method = 'DENFIS'`)

For regression using package frbs with tuning parameters:

- Threshold (`Dthr`, numeric)

- Max. Iterations (`max.iter`, numeric)

Elasticnet (`method = 'enet'`)

For regression using package elasticnet with tuning parameters:

- Fraction of Full Solution (`fraction`, numeric)

- Weight Decay (`lambda`, numeric)

Ensemble Partial Least Squares Regression (`method = 'enpls'`)

For regression using package enpls with tuning parameters:

- Max. Number of Components (`maxcomp`, numeric)

Ensemble Partial Least Squares Regression with Feature Selection (`method = 'enpls.fs'`)

For regression using package enpls with tuning parameters:

- Max. Number of Components (`maxcomp`, numeric)

- Importance Cutoff (`threshold`, numeric)

Ensembles of Generalized Lienar Models (`method = 'randomGLM'`)

For classification and regression using package randomGLM with tuning parameters:

- Interaction Order (`maxInteractionOrder`, numeric)

eXtreme Gradient Boosting (`method = 'xgbLinear'`)

For classification and regression using package xgboost with tuning parameters:

- Number of Boosting Iterations (`nrounds`, numeric)

- L2 Regularization (`lambda`, numeric)

- L1 Regularization (`alpha`, numeric)

- Learning Rate (`eta`, numeric)

eXtreme Gradient Boosting (`method = 'xgbTree'`)

For classification and regression using packages xgboost and plyr with tuning parameters:

- Number of Boosting Iterations (`nrounds`, numeric)

- Max Tree Depth (`max_depth`, numeric)

- Shrinkage (`eta`, numeric)

- Minimum Loss Reduction (`gamma`, numeric)

- Subsample Ratio of Columns (`colsample_bytree`, numeric)

- Minimum Sum of Instance Weight (`min_child_weight`, numeric)

Extreme Learning Machine (`method = 'elm'`)

For classification and regression using package elmNN with tuning parameters:

- Number of Hidden Units (`nhid`, numeric)

- Activation Function (`actfun`, character)

Factor-Based Linear Discriminant Analysis (`method = 'RFlda'`)

For classification using package HiDimDA with tuning parameters:

- Number of Factors (`q`, numeric)

Flexible Discriminant Analysis (`method = 'fda'`)

For classification using packages earth and mda with tuning parameters:

- Product Degree (`degree`, numeric)

- Number of Terms (`nprune`, numeric)

Fuzzy Inference Rules by Descent Method (`method = 'FIR.DM'`)

For regression using package frbs with tuning parameters:

- Number of Fuzzy Terms (`num.labels`, numeric)

- Max. Iterations (`max.iter`, numeric)

Fuzzy Rules Using Chi's Method (`method = 'FRBCS.CHI'`)

For classification using package frbs with tuning parameters:

- Number of Fuzzy Terms (`num.labels`, numeric)

- Membership Function (`type.mf`, character)

Fuzzy Rules Using Genetic Cooperative-Competitive Learning (`method = 'GFS.GCCL'`)

For classification using package frbs with tuning parameters:

- Number of Fuzzy Terms (`num.labels`, numeric)

- Population Size (`popu.size`, numeric)

- Max. Generations (`max.gen`, numeric)

Fuzzy Rules Using Genetic Cooperative-Competitive Learning and Pittsburgh (`method = 'FH.GBML'`)

For classification using package frbs with tuning parameters:

- Max. Number of Rules (`max.num.rule`, numeric)

- Population Size (`popu.size`, numeric)

- Max. Generations (`max.gen`, numeric)

Fuzzy Rules Using the Structural Learning Algorithm on Vague Environment (`method = 'SLAVE'`)

For classification using package frbs with tuning parameters:

- Number of Fuzzy Terms (`num.labels`, numeric)

- Max. Iterations (`max.iter`, numeric)

- Max. Generations (`max.gen`, numeric)

Fuzzy Rules via MOGUL (`method = 'GFS.FR.MOGUL'`)

For regression using package frbs with tuning parameters:

- Max. Generations (`max.gen`, numeric)

- Max. Iterations (`max.iter`, numeric)

- Max. Tuning Iterations (`max.tune`, numeric)

Fuzzy Rules via Thrift (`method = 'GFS.THRIFT'`)

For regression using package frbs with tuning parameters:

- Population Size (`popu.size`, numeric)

- Number of Fuzzy Labels (`num.labels`, numeric)

- Max. Generations (`max.gen`, numeric)

Fuzzy Rules with Weight Factor (`method = 'FRBCS.W'`)

For classification using package frbs with tuning parameters:

- Number of Fuzzy Terms (`num.labels`, numeric)

- Membership Function (`type.mf`, character)

Gaussian Process (`method = 'gaussprLinear'`)

For classification and regression using package kernlab with no tuning parameters

Gaussian Process with Polynomial Kernel (`method = 'gaussprPoly'`)

For classification and regression using package kernlab with tuning parameters:

- Polynomial Degree (`degree`, numeric)

- Scale (`scale`, numeric)

Gaussian Process with Radial Basis Function Kernel (`method = 'gaussprRadial'`)

For classification and regression using package kernlab with tuning parameters:

- Sigma (`sigma`, numeric)

Generalized Additive Model using LOESS (`method = 'gamLoess'`)

For classification and regression using package gam with tuning parameters:

- Span (`span`, numeric)

- Degree (`degree`, numeric)

Generalized Additive Model using Splines (`method = 'bam'`)

For classification and regression using package mgcv with tuning parameters:

- Feature Selection (`select`, logical)

- Method (`method`, character)

Generalized Additive Model using Splines (`method = 'gam'`)

For classification and regression using package mgcv with tuning parameters:

- Feature Selection (`select`, logical)

- Method (`method`, character)

Generalized Additive Model using Splines (`method = 'gamSpline'`)

For classification and regression using package gam with tuning parameters:

- Degrees of Freedom (`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 MASS with no tuning parameters

Generalized Partial Least Squares (`method = 'gpls'`)

For classification using package gpls with tuning parameters:

- Number of Components (`K.prov`, numeric)

Genetic Lateral Tuning and Rule Selection of Linguistic Fuzzy Systems (`method = 'GFS.LT.RS'`)

For regression using package frbs with tuning parameters:

- Population Size (`popu.size`, numeric)

- Number of Fuzzy Labels (`num.labels`, numeric)

- Max. Generations (`max.gen`, numeric)

glmnet (`method = 'glmnet'`)

For classification and regression using package glmnet with tuning parameters:

- Mixing Percentage (`alpha`, numeric)

- Regularization Parameter (`lambda`, numeric)

Greedy Prototype Selection (`method = 'protoclass'`)

For classification using packages proxy and protoclass with tuning parameters:

- Ball Size (`eps`, numeric)

- Distance Order (`Minkowski`, numeric)

Heteroscedastic Discriminant Analysis (`method = 'hda'`)

For classification using package hda with tuning parameters:

- Gamma (`gamma`, numeric)

- Lambda (`lambda`, numeric)

- Dimension of the Discriminative Subspace (`newdim`, numeric)

High Dimensional Discriminant Analysis (`method = 'hdda'`)

For classification using package HDclassif with tuning parameters:

- Threshold (`threshold`, character)

- Model Type (`model`, numeric)

High-Dimensional Regularized Discriminant Analysis (`method = 'hdrda'`)

For classification using package sparsediscrim with tuning parameters:

- Gamma (`gamma`, numeric)

- Lambda (`lambda`, numeric)

- Shrinkage Type (`shrinkage_type`, character)

Hybrid Neural Fuzzy Inference System (`method = 'HYFIS'`)

For regression using package frbs with tuning parameters:

- Number of Fuzzy Terms (`num.labels`, numeric)

- Max. Iterations (`max.iter`, numeric)

Independent Component Regression (`method = 'icr'`)

For regression using package fastICA with tuning parameters:

- Number of Components (`n.comp`, numeric)

k-Nearest Neighbors (`method = 'kknn'`)

For classification and regression using package kknn with tuning parameters:

- Max. Number of Neighbors (`kmax`, numeric)

- Distance (`distance`, numeric)

- Kernel (`kernel`, character)

k-Nearest Neighbors (`method = 'knn'`)

For classification and regression with tuning parameters:

- Number of Neighbors (`k`, numeric)

Knn regression via sklearn.neighbors.KNeighborsRegressor (`method = 'pythonKnnReg'`)

For regression using package rPython with tuning parameters:

- Number of Neighbors (`n_neighbors`, numeric)

- Weight Function (`weights`, character)

- Algorithm (`algorithm`, character)

- Leaf Size (`leaf_size`, numeric)

- Distance Metric (`metric`, character)

- p (`p`, numeric)

L2 Regularized Linear Support Vector Machines with Class Weights (`method = 'svmLinearWeights2'`)

For classification using package LiblineaR with tuning parameters:

- Cost (`cost`, numeric)

- Loss Function (`Loss`, character)

- Class Weight (`weight`, numeric)

L2 Regularized Support Vector Machine (dual) with Linear Kernel (`method = 'svmLinear3'`)

For classification and regression using package LiblineaR with tuning parameters:

- Cost (`cost`, numeric)

- Loss Function (`Loss`, character)

Learning Vector Quantization (`method = 'lvq'`)

For classification using package class with tuning parameters:

- Codebook Size (`size`, numeric)

- Number of Prototypes (`k`, numeric)

Least Angle Regression (`method = 'lars'`)

For regression using package lars with tuning parameters:

- Fraction (`fraction`, numeric)

Least Angle Regression (`method = 'lars2'`)

For regression using package lars with tuning parameters:

- Number of Steps (`step`, numeric)

Least Squares Support Vector Machine (`method = 'lssvmLinear'`)

For classification using package kernlab with tuning parameters:

- Regularization Parameter (`tau`, numeric)

Least Squares Support Vector Machine with Polynomial Kernel (`method = 'lssvmPoly'`)

For classification using package kernlab with tuning parameters:

- Polynomial Degree (`degree`, numeric)

- Scale (`scale`, numeric)

- Regularization Parameter (`tau`, numeric)

Least Squares Support Vector Machine with Radial Basis Function Kernel (`method = 'lssvmRadial'`)

For classification using package kernlab with tuning parameters:

- Sigma (`sigma`, numeric)

- Regularization Parameter (`tau`, numeric)

Linear Discriminant Analysis (`method = 'lda'`)

For classification using package MASS with no tuning parameters

Linear Discriminant Analysis (`method = 'lda2'`)

For classification using package MASS with tuning parameters:

- Number of Discriminant Functions (`dimen`, numeric)

Linear Discriminant Analysis with Stepwise Feature Selection (`method = 'stepLDA'`)

For classification using packages klaR and MASS with tuning parameters:

- Maximum Number of Variables (`maxvar`, numeric)

- Search Direction (`direction`, character)

Linear Distance Weighted Discrimination (`method = 'dwdLinear'`)

For classification using package kerndwd with tuning parameters:

- Regularization Parameter (`lambda`, numeric)

- q (`qval`, numeric)

Linear Regression (`method = 'lm'`)

For regression with tuning parameters:

- intercept (`intercept`, logical)

Linear Regression with Backwards Selection (`method = 'leapBackward'`)

For regression using package leaps with tuning parameters:

- Maximum Number of Predictors (`nvmax`, numeric)

Linear Regression with Forward Selection (`method = 'leapForward'`)

For regression using package leaps with tuning parameters:

- Maximum Number of Predictors (`nvmax`, numeric)

Linear Regression with Stepwise Selection (`method = 'leapSeq'`)

For regression using package leaps with tuning parameters:

- Maximum Number of Predictors (`nvmax`, numeric)

Linear Regression with Stepwise Selection (`method = 'lmStepAIC'`)

For regression using package MASS with no tuning parameters

Linear Support Vector Machines with Class Weights (`method = 'svmLinearWeights'`)

For classification using package e1071 with tuning parameters:

- Cost (`cost`, numeric)

- Class Weight (`weight`, numeric)

Localized Linear Discriminant Analysis (`method = 'loclda'`)

For classification using package klaR with tuning parameters:

- Number of Nearest Neighbors (`k`, numeric)

Logic Regression (`method = 'logreg'`)

For classification and regression using package LogicReg with tuning parameters:

- Maximum Number of Leaves (`treesize`, numeric)

- Number of Trees (`ntrees`, numeric)

Logistic Model Trees (`method = 'LMT'`)

For classification using package RWeka with tuning parameters:

- Number of Iteratons (`iter`, numeric)

Maximum Uncertainty Linear Discriminant Analysis (`method = 'Mlda'`)

For classification using package HiDimDA with no tuning parameters

Mixture Discriminant Analysis (`method = 'mda'`)

For classification using package mda with tuning parameters:

- Number of Subclasses Per Class (`subclasses`, numeric)

Model Averaged Naive Bayes Classifier (`method = 'manb'`)

For classification using package bnclassify with tuning parameters:

- Smoothing Parameter (`smooth`, numeric)

- Prior Probability (`prior`, numeric)

Model Averaged Neural Network (`method = 'avNNet'`)

For classification and regression using package nnet with tuning parameters:

- Number of Hidden Units (`size`, numeric)

- Weight Decay (`decay`, numeric)

- Bagging (`bag`, logical)

Model Rules (`method = 'M5Rules'`)

For regression using package RWeka with tuning parameters:

- Pruned (`pruned`, character)

- Smoothed (`smoothed`, character)

Model Tree (`method = 'M5'`)

For regression using package RWeka with tuning parameters:

- Pruned (`pruned`, character)

- Smoothed (`smoothed`, character)

- Rules (`rules`, character)

Multi-Layer Perceptron (`method = 'mlp'`)

For classification and regression using package RSNNS with tuning parameters:

- Number of Hidden Units (`size`, numeric)

Multi-Layer Perceptron (`method = 'mlpWeightDecay'`)

For classification and regression using package RSNNS with tuning parameters:

- Number of Hidden Units (`size`, numeric)

- Weight Decay (`decay`, numeric)

Multi-Layer Perceptron, multiple layers (`method = 'mlpWeightDecayML'`)

For classification and regression using package RSNNS with tuning parameters:

- Number of Hidden Units layer1 (`layer1`, numeric)

- Number of Hidden Units layer2 (`layer2`, numeric)

- Number of Hidden Units layer3 (`layer3`, numeric)

- Weight Decay (`decay`, numeric)

Multi-Layer Perceptron, with multiple layers (`method = 'mlpML'`)

For classification and regression using package RSNNS with tuning parameters:

- Number of Hidden Units layer1 (`layer1`, numeric)

- Number of Hidden Units layer2 (`layer2`, numeric)

- Number of Hidden Units layer3 (`layer3`, numeric)

Multilayer Perceptron Network by Stochastic Gradient Descent (`method = 'mlpSGD'`)

For regression using package FCNN4R with tuning parameters:

- Number of Hidden Units (`size`, numeric)

- L2 Regularization (`l2reg`, numeric)

- RMSE Gradient Scaling (`lambda`, numeric)

- Learning Rate (`learn_rate`, numeric)

- Momentum (`momentum`, numeric)

- Decay (`gamma`, numeric)

- Batch Size (`minibatchsz`, numeric)

- Number of Models (`repeats`, numeric)

Multivariate Adaptive Regression Spline (`method = 'earth'`)

For classification and regression using package earth with tuning parameters:

- Number of Terms (`nprune`, numeric)

- Product Degree (`degree`, numeric)

Multivariate Adaptive Regression Splines (`method = 'gcvEarth'`)

For classification and regression using package earth with tuning parameters:

- Product Degree (`degree`, numeric)

Naive Bayes (`method = 'nb'`)

For classification using package klaR with tuning parameters:

- Laplace Correction (`fL`, numeric)

- Distribution Type (`usekernel`, logical)

- Bandwidth Adjustment (`adjust`, numeric)

Naive Bayes Classifier (`method = 'nbDiscrete'`)

For classification using package bnclassify with tuning parameters:

- Smoothing Parameter (`smooth`, numeric)

Naive Bayes Classifier with Attribute Weighting (`method = 'awnb'`)

For classification using package bnclassify with tuning parameters:

- Smoothing Parameter (`smooth`, numeric)

Nearest Shrunken Centroids (`method = 'pam'`)

For classification using package pamr with tuning parameters:

- Shrinkage Threshold (`threshold`, numeric)

Neural Network (`method = 'neuralnet'`)

For regression using package neuralnet with tuning parameters:

- Number of Hidden Units in Layer 1 (`layer1`, numeric)

- Number of Hidden Units in Layer 2 (`layer2`, numeric)

- Number of Hidden Units in Layer 3 (`layer3`, numeric)

Neural Network (`method = 'nnet'`)

For classification and regression using package nnet with tuning parameters:

- Number of Hidden Units (`size`, numeric)

- Weight Decay (`decay`, numeric)

Neural Networks with Feature Extraction (`method = 'pcaNNet'`)

For classification and regression using package nnet with tuning parameters:

- Number of Hidden Units (`size`, numeric)

- Weight Decay (`decay`, numeric)

Non-Convex Penalized Quantile Regression (`method = 'rqnc'`)

For regression using package rqPen with tuning parameters:

- L1 Penalty (`lambda`, numeric)

- Penalty Type (`penalty`, character)

Non-Negative Least Squares (`method = 'nnls'`)

For regression using package nnls with no tuning parameters

Oblique Random Forest (`method = 'ORFlog'`)

For classification using package obliqueRF with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

Oblique Random Forest (`method = 'ORFpls'`)

For classification using package obliqueRF with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

Oblique Random Forest (`method = 'ORFridge'`)

For classification using package obliqueRF with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

Oblique Random Forest (`method = 'ORFsvm'`)

For classification using package obliqueRF with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

Oblique Trees (`method = 'oblique.tree'`)

For classification using package oblique.tree with tuning parameters:

- Oblique Splits (`oblique.splits`, character)

- Variable Selection Method (`variable.selection`, character)

Optimal Weighted Nearest Neighbor Classifier (`method = 'ownn'`)

For classification using package snn with tuning parameters:

- Number of Neighbors (`K`, numeric)

Ordered Logistic or Probit Regression (`method = 'polr'`)

For classification using package MASS with tuning parameters:

- parameter (`method`, character)

Parallel Random Forest (`method = 'parRF'`)

For classification and regression using packages e1071, randomForest and foreach with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

partDSA (`method = 'partDSA'`)

For classification and regression using package partDSA with tuning parameters:

- Number of Terminal Partitions (`cut.off.growth`, numeric)

- Minimum Percent Difference (`MPD`, numeric)

Partial Least Squares (`method = 'kernelpls'`)

For classification and regression using package pls with tuning parameters:

- Number of Components (`ncomp`, numeric)

Partial Least Squares (`method = 'pls'`)

For classification and regression using package pls with tuning parameters:

- Number of Components (`ncomp`, numeric)

Partial Least Squares (`method = 'simpls'`)

For classification and regression using package pls with tuning parameters:

- Number of Components (`ncomp`, numeric)

Partial Least Squares (`method = 'widekernelpls'`)

For classification and regression using package pls with tuning parameters:

- Number of Components (`ncomp`, numeric)

Partial Least Squares Generalized Linear Models (`method = 'plsRglm'`)

For classification and regression using package plsRglm with tuning parameters:

- Number of PLS Components (`nt`, numeric)

- p-Value threshold (`alpha.pvals.expli`, numeric)

Penalized Discriminant Analysis (`method = 'pda'`)

For classification using package mda with tuning parameters:

- Shrinkage Penalty Coefficient (`lambda`, numeric)

Penalized Discriminant Analysis (`method = 'pda2'`)

For classification using package mda with tuning parameters:

- Degrees of Freedom (`df`, numeric)

Penalized Linear Discriminant Analysis (`method = 'PenalizedLDA'`)

For classification using packages penalizedLDA and plyr with tuning parameters:

- L1 Penalty (`lambda`, numeric)

- Number of Discriminant Functions (`K`, numeric)

Penalized Linear Regression (`method = 'penalized'`)

For regression using package penalized with tuning parameters:

- L1 Penalty (`lambda1`, numeric)

- L2 Penalty (`lambda2`, numeric)

Penalized Logistic Regression (`method = 'plr'`)

For classification using package stepPlr with tuning parameters:

- L2 Penalty (`lambda`, numeric)

- Complexity Parameter (`cp`, character)

Penalized Multinomial Regression (`method = 'multinom'`)

For classification using package nnet with tuning parameters:

- Weight Decay (`decay`, numeric)

Penalized Ordinal Regression (`method = 'ordinalNet'`)

For classification and regression using packages ordinalNet and plyr with tuning parameters:

- Mixing Percentage (`alpha`, numeric)

- Selection Criterion (`criteria`, character)

- Link Function (`link`, character)

Polynomial Kernel Regularized Least Squares (`method = 'krlsPoly'`)

For regression using package KRLS with tuning parameters:

- Regularization Parameter (`lambda`, numeric)

- Polynomial Degree (`degree`, numeric)

Principal Component Analysis (`method = 'pcr'`)

For regression using package pls with tuning parameters:

- Number of Components (`ncomp`, numeric)

Projection Pursuit Regression (`method = 'ppr'`)

For regression with tuning parameters:

- Number of Terms (`nterms`, numeric)

Quadratic Discriminant Analysis (`method = 'qda'`)

For classification using package MASS with no tuning parameters

Quadratic Discriminant Analysis with Stepwise Feature Selection (`method = 'stepQDA'`)

For classification using packages klaR and MASS with tuning parameters:

- Maximum Number of Variables (`maxvar`, numeric)

- Search Direction (`direction`, character)

Quantile Random Forest (`method = 'qrf'`)

For regression using package quantregForest with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

Quantile Regression Neural Network (`method = 'qrnn'`)

For regression using package qrnn with tuning parameters:

- Number of Hidden Units (`n.hidden`, numeric)

- Weight Decay (`penalty`, numeric)

- Bagged Models? (`bag`, logical)

Quantile Regression with LASSO penalty (`method = 'rqlasso'`)

For regression using package rqPen with tuning parameters:

- L1 Penalty (`lambda`, numeric)

Radial Basis Function Kernel Regularized Least Squares (`method = 'krlsRadial'`)

For regression using packages KRLS and kernlab with tuning parameters:

- Regularization Parameter (`lambda`, numeric)

- Sigma (`sigma`, numeric)

Radial Basis Function Network (`method = 'rbf'`)

For classification and regression using package RSNNS with tuning parameters:

- Number of Hidden Units (`size`, numeric)

Radial Basis Function Network (`method = 'rbfDDA'`)

For classification and regression using package RSNNS with tuning parameters:

- Activation Limit for Conflicting Classes (`negativeThreshold`, numeric)

Random Ferns (`method = 'rFerns'`)

For classification using package rFerns with tuning parameters:

- Fern Depth (`depth`, numeric)

Random Forest (`method = 'ranger'`)

For classification and regression using packages e1071 and ranger with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

Random Forest (`method = 'Rborist'`)

For classification and regression using package Rborist with tuning parameters:

- Number of Randomly Selected Predictors (`predFixed`, numeric)

Random Forest (`method = 'rf'`)

For classification and regression using package randomForest with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

Random Forest by Randomization (`method = 'extraTrees'`)

For classification and regression using package extraTrees with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

- Number of Random Cuts (`numRandomCuts`, numeric)

Random Forest Rule-Based Model (`method = 'rfRules'`)

For classification and regression using packages randomForest, inTrees and plyr with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

- Maximum Rule Depth (`maxdepth`, numeric)

Random Forest with Additional Feature Selection (`method = 'Boruta'`)

For classification and regression using packages Boruta and randomForest with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

Regularized Discriminant Analysis (`method = 'rda'`)

For classification using package klaR with tuning parameters:

- Gamma (`gamma`, numeric)

- Lambda (`lambda`, numeric)

Regularized Linear Discriminant Analysis (`method = 'rlda'`)

For classification using package sparsediscrim with tuning parameters:

- Regularization Method (`estimator`, character)

Regularized Random Forest (`method = 'RRF'`)

For classification and regression using packages randomForest and RRF with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

- Regularization Value (`coefReg`, numeric)

- Importance Coefficient (`coefImp`, numeric)

Regularized Random Forest (`method = 'RRFglobal'`)

For classification and regression using package RRF with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

- Regularization Value (`coefReg`, numeric)

Relaxed Lasso (`method = 'relaxo'`)

For regression using packages relaxo and plyr with tuning parameters:

- Penalty Parameter (`lambda`, numeric)

- Relaxation Parameter (`phi`, numeric)

Relevance Vector Machines with Linear Kernel (`method = 'rvmLinear'`)

For regression using package kernlab with no tuning parameters

Relevance Vector Machines with Polynomial Kernel (`method = 'rvmPoly'`)

For regression using package kernlab with tuning parameters:

- Scale (`scale`, numeric)

- Polynomial Degree (`degree`, numeric)

Relevance Vector Machines with Radial Basis Function Kernel (`method = 'rvmRadial'`)

For regression using package kernlab with tuning parameters:

- Sigma (`sigma`, numeric)

Ridge Regression (`method = 'ridge'`)

For regression using package elasticnet with tuning parameters:

- Weight Decay (`lambda`, numeric)

Ridge Regression with Variable Selection (`method = 'foba'`)

For regression using package foba with tuning parameters:

- Number of Variables Retained (`k`, numeric)

- L2 Penalty (`lambda`, numeric)

Robust Linear Discriminant Analysis (`method = 'Linda'`)

For classification using package rrcov with no tuning parameters

Robust Linear Model (`method = 'rlm'`)

For regression using package MASS with tuning parameters:

- intercept (`intercept`, logical)

- psi (`psi`, character)

Robust Mixture Discriminant Analysis (`method = 'rmda'`)

For classification using package robustDA with tuning parameters:

- Number of Subclasses Per Class (`K`, numeric)

- Model (`model`, character)

Robust Quadratic Discriminant Analysis (`method = 'QdaCov'`)

For classification using package rrcov with no tuning parameters

Robust Regularized Linear Discriminant Analysis (`method = 'rrlda'`)

For classification using package rrlda with tuning parameters:

- Penalty Parameter (`lambda`, numeric)

- Robustness Parameter (`hp`, numeric)

- Penalty Type (`penalty`, character)

Robust SIMCA (`method = 'RSimca'`)

For classification using package rrcovHD with no tuning parameters

ROC-Based Classifier (`method = 'rocc'`)

For classification using package rocc with tuning parameters:

- Number of Variables Retained (`xgenes`, numeric)

Rotation Forest (`method = 'rotationForest'`)

For classification using package rotationForest with tuning parameters:

- Number of Variable Subsets (`K`, numeric)

- Ensemble Size (`L`, numeric)

Rotation Forest (`method = 'rotationForestCp'`)

For classification using packages rpart, plyr and rotationForest with tuning parameters:

- Number of Variable Subsets (`K`, numeric)

- Ensemble Size (`L`, numeric)

- Complexity Parameter (`cp`, numeric)

Rule-Based Classifier (`method = 'JRip'`)

For classification using package RWeka with tuning parameters:

- Number of Optimizations (`NumOpt`, numeric)

Rule-Based Classifier (`method = 'PART'`)

For classification using package RWeka with tuning parameters:

- Confidence Threshold (`threshold`, numeric)

- Confidence Threshold (`pruned`, character)

Self-Organizing Map (`method = 'bdk'`)

For classification and regression using package kohonen with tuning parameters:

- Row (`xdim`, numeric)

- Columns (`ydim`, numeric)

- X Weight (`xweight`, numeric)

- Topology (`topo`, character)

Self-Organizing Maps (`method = 'xyf'`)

For classification and regression using package kohonen with tuning parameters:

- Row (`xdim`, numeric)

- Columns (`ydim`, numeric)

- X Weight (`xweight`, numeric)

- Topology (`topo`, character)

Semi-Naive Structure Learner Wrapper (`method = 'nbSearch'`)

For classification using package bnclassify with tuning parameters:

- Number of Folds (`k`, numeric)

- Minimum Absolute Improvement (`epsilon`, numeric)

- Smoothing Parameter (`smooth`, numeric)

- Final Smoothing Parameter (`final_smooth`, numeric)

- Search Direction (`direction`, character)

Shrinkage Discriminant Analysis (`method = 'sda'`)

For classification using package sda with tuning parameters:

- Diagonalize (`diagonal`, logical)

- shrinkage (`lambda`, numeric)

SIMCA (`method = 'CSimca'`)

For classification using package rrcovHD with no tuning parameters

Simplified TSK Fuzzy Rules (`method = 'FS.HGD'`)

For regression using package frbs with tuning parameters:

- Number of Fuzzy Terms (`num.labels`, numeric)

- Max. Iterations (`max.iter`, numeric)

Single C5.0 Ruleset (`method = 'C5.0Rules'`)

For classification using package C50 with no tuning parameters

Single C5.0 Tree (`method = 'C5.0Tree'`)

For classification using package C50 with no tuning parameters

Single Rule Classification (`method = 'OneR'`)

For classification using package RWeka with no tuning parameters

Sparse Distance Weighted Discrimination (`method = 'sdwd'`)

For classification using package sdwd with tuning parameters:

- L1 Penalty (`lambda`, numeric)

- L2 Penalty (`lambda2`, numeric)

Sparse Linear Discriminant Analysis (`method = 'sparseLDA'`)

For classification using package sparseLDA with tuning parameters:

- Number of Predictors (`NumVars`, numeric)

- Lambda (`lambda`, numeric)

Sparse Mixture Discriminant Analysis (`method = 'smda'`)

For classification using package sparseLDA with tuning parameters:

- Number of Predictors (`NumVars`, numeric)

- Lambda (`lambda`, numeric)

- Number of Subclasses (`R`, numeric)

Sparse Partial Least Squares (`method = 'spls'`)

For classification and regression using package spls with tuning parameters:

- Number of Components (`K`, numeric)

- Threshold (`eta`, numeric)

- Kappa (`kappa`, numeric)

Spike and Slab Regression (`method = 'spikeslab'`)

For regression using packages spikeslab and plyr with tuning parameters:

- Variables Retained (`vars`, numeric)

Stabilized Linear Discriminant Analysis (`method = 'slda'`)

For classification using package ipred with no tuning parameters

Stabilized Nearest Neighbor Classifier (`method = 'snn'`)

For classification using package snn with tuning parameters:

- Stabilization Parameter (`lambda`, numeric)

Stacked AutoEncoder Deep Neural Network (`method = 'dnn'`)

For classification and regression using package deepnet with tuning parameters:

- Hidden Layer 1 (`layer1`, numeric)

- Hidden Layer 2 (`layer2`, numeric)

- Hidden Layer 3 (`layer3`, numeric)

- Hidden Dropouts (`hidden_dropout`, numeric)

- Visible Dropout (`visible_dropout`, numeric)

Stepwise Diagonal Linear Discriminant Analysis (`method = 'sddaLDA'`)

For classification using package SDDA with no tuning parameters

Stepwise Diagonal Quadratic Discriminant Analysis (`method = 'sddaQDA'`)

For classification using package SDDA with no tuning parameters

Stochastic Gradient Boosting (`method = 'gbm'`)

For classification and regression using packages gbm and plyr with tuning parameters:

- Number of Boosting Iterations (`n.trees`, numeric)

- Max Tree Depth (`interaction.depth`, numeric)

- Shrinkage (`shrinkage`, numeric)

- Min. Terminal Node Size (`n.minobsinnode`, numeric)

Subtractive Clustering and Fuzzy c-Means Rules (`method = 'SBC'`)

For regression using package frbs with tuning parameters:

- Radius (`r.a`, numeric)

- Upper Threshold (`eps.high`, numeric)

- Lower Threshold (`eps.low`, numeric)

Supervised Principal Component Analysis (`method = 'superpc'`)

For regression using package superpc with tuning parameters:

- Threshold (`threshold`, numeric)

- Number of Components (`n.components`, numeric)

Support Vector Machines with Boundrange String Kernel (`method = 'svmBoundrangeString'`)

For classification and regression using package kernlab with tuning parameters:

- length (`length`, numeric)

- Cost (`C`, numeric)

Support Vector Machines with Class Weights (`method = 'svmRadialWeights'`)

For classification using package kernlab with tuning parameters:

- Sigma (`sigma`, numeric)

- Cost (`C`, numeric)

- Weight (`Weight`, numeric)

Support Vector Machines with Exponential String Kernel (`method = 'svmExpoString'`)

For classification and regression using package kernlab with tuning parameters:

- lambda (`lambda`, numeric)

- Cost (`C`, numeric)

Support Vector Machines with Linear Kernel (`method = 'svmLinear'`)

For classification and regression using package kernlab with tuning parameters:

- Cost (`C`, numeric)

Support Vector Machines with Linear Kernel (`method = 'svmLinear2'`)

For classification and regression using package e1071 with tuning parameters:

- Cost (`cost`, numeric)

Support Vector Machines with Polynomial Kernel (`method = 'svmPoly'`)

For classification and regression using package kernlab with tuning parameters:

- Polynomial Degree (`degree`, numeric)

- Scale (`scale`, numeric)

- Cost (`C`, numeric)

Support Vector Machines with Radial Basis Function Kernel (`method = 'svmRadial'`)

For classification and regression using package kernlab with tuning parameters:

- Sigma (`sigma`, numeric)

- Cost (`C`, numeric)

Support Vector Machines with Radial Basis Function Kernel (`method = 'svmRadialCost'`)

For classification and regression using package kernlab with tuning parameters:

- Cost (`C`, numeric)

Support Vector Machines with Radial Basis Function Kernel (`method = 'svmRadialSigma'`)

For classification and regression using package kernlab with tuning parameters:

- Sigma (`sigma`, numeric)

- Cost (`C`, numeric)

Support Vector Machines with Spectrum String Kernel (`method = 'svmSpectrumString'`)

For classification and regression using package kernlab with tuning parameters:

- length (`length`, numeric)

- Cost (`C`, numeric)

The Bayesian lasso (`method = 'blasso'`)

For regression using package monomvn with tuning parameters:

- Sparsity Threshold (`sparsity`, numeric)

The lasso (`method = 'lasso'`)

For regression using package elasticnet with tuning parameters:

- Fraction of Full Solution (`fraction`, numeric)

Tree Augmented Naive Bayes Classifier (`method = 'tan'`)

For classification using package bnclassify with tuning parameters:

- Score Function (`score`, character)

- Smoothing Parameter (`smooth`, numeric)

Tree Augmented Naive Bayes Classifier Structure Learner Wrapper (`method = 'tanSearch'`)

For classification using package bnclassify with tuning parameters:

- Number of Folds (`k`, numeric)

- Minimum Absolute Improvement (`epsilon`, numeric)

- Smoothing Parameter (`smooth`, numeric)

- Final Smoothing Parameter (`final_smooth`, numeric)

- Super-Parent (`sp`, logical)

Tree Augmented Naive Bayes Classifier with Attribute Weighting (`method = 'awtan'`)

For classification using package bnclassify with tuning parameters:

- Score Function (`score`, character)

- Smoothing Parameter (`smooth`, numeric)

Tree Models from Genetic Algorithms (`method = 'evtree'`)

For classification and regression using package evtree with tuning parameters:

- Complexity Parameter (`alpha`, numeric)

Tree-Based Ensembles (`method = 'nodeHarvest'`)

For classification and regression using package nodeHarvest with tuning parameters:

- Maximum Interaction Depth (`maxinter`, numeric)

- Prediction Mode (`mode`, character)

Variational Bayesian Multinomial Probit Regression (`method = 'vbmpRadial'`)

For classification using package vbmp with tuning parameters:

- Theta Estimated (`estimateTheta`, character)

Wang and Mendel Fuzzy Rules (`method = 'WM'`)

For regression using package frbs with tuning parameters:

- Number of Fuzzy Terms (`num.labels`, numeric)

- Membership Function (`type.mf`, character)

Weighted Subspace Random Forest (`method = 'wsrf'`)

For classification using package wsrf with tuning parameters:

- Number of Randomly Selected Predictors (`mtry`, numeric)

### References

“Using your own model in `train`” (http://caret.r-forge.r-project.org/custom_models.html)

[Package caret version 6.0-73 Index]