train_model_list: 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.

Bagged CART (method = 'treebag')

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

Bagged Flexible Discriminant Analysis (method = 'bagFDA')

For classification using packages earth and mda with tuning parameters:

Product Degree (degree)
Number of Terms (nprune)

Bagged Logic Regression (method = 'logicBag')

For classification and regression using package logicFS with tuning parameters:

Maximum Number of Leaves (nleaves)
Number of Trees (ntrees)

Bagged MARS (method = 'bagEarth')

For classification and regression using package earth with tuning parameters:

Number of Terms (nprune)
Product Degree (degree)

Bagged Model (method = 'bag')

For classification and regression using package caret with tuning parameters:

Number of Randomly Selected Predictors (vars)

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)

Boosted Classification Trees (method = 'ada')

For classification using package ada with tuning parameters:

Number of Trees (iter)
Max Tree Depth (maxdepth)
Learning Rate (nu)

Boosted Generalized Additive Model (method = 'gamboost')

For classification and regression using package mboost with tuning parameters:

Number of Boosting Iterations (mstop)
AIC Prune? (prune)

Boosted Generalized Linear Model (method = 'glmboost')

For classification and regression using package mboost with tuning parameters:

Number of Boosting Iterations (mstop)
AIC Prune? (prune)

Boosted Linear Model (method = 'bstLs')

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

Number of Boosting Iterations (mstop)
Shrinkage (nu)

Boosted Logistic Regression (method = 'LogitBoost')

For classification using package caTools with tuning parameters:

Number of Boosting Iterations (nIter)

Boosted Smoothing Spline (method = 'bstSm')

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

Number of Boosting Iterations (mstop)
Shrinkage (nu)

Boosted Tree (method = 'blackboost')

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

Number of Trees (mstop)
Max Tree Depth (maxdepth)

Boosted Tree (method = 'bstTree')

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

Number of Boosting Iterations (mstop)
Max Tree Depth (maxdepth)
Shrinkage (nu)

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

For classification using package RWeka with tuning parameters:

Confidence Threshold (C)

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

For classification using packages C50 and plyr with tuning parameters:

Number of Boosting Iterations (trials)
Model Type (model)
Winnow (winnow)

CART (method = 'rpart')

For classification and regression using package rpart with tuning parameters:

Complexity Parameter (cp)

CART (method = 'rpart2')

For classification and regression using package rpart with tuning parameters:

Max Tree Depth (maxdepth)

Conditional Inference Random Forest (method = 'cforest')

For classification and regression using package party with tuning parameters:

Number of Randomly Selected Predictors (mtry)

Conditional Inference Tree (method = 'ctree')

For classification and regression using package party with tuning parameters:

1 - P-Value Threshold (mincriterion)

Conditional Inference Tree (method = 'ctree2')

For classification and regression using package party with tuning parameters:

Max Tree Depth (maxdepth)

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

For classification using packages C50 and plyr with tuning parameters:

Number of Boosting Iterations (trials)
Model Type (model)
Winnow (winnow)
Cost (cost)

Cost-Sensitive CART (method = 'rpartCost')

For classification using package rpart with tuning parameters:

Complexity Parameter (cp)
Cost (Cost)

Cubist (method = 'cubist')

For regression using package Cubist with tuning parameters:

Number of Committees (committees)
Number of Instances (neighbors)

Elasticnet (method = 'enet')

For regression using package elasticnet with tuning parameters:

Fraction of Full Solution (fraction)
Weight Decay (lambda)

Extreme Learning Machine (method = 'elm')

For classification and regression using package elmNN with tuning parameters:

Number of Hidden Units (nhid)
Activation Function (actfun)

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

For classification using package HiDimDA with tuning parameters:

Number of Factors (q)

Flexible Discriminant Analysis (method = 'fda')

For classification using packages earth and mda with tuning parameters:

Product Degree (degree)
Number of Terms (nprune)

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)
Scale (scale)

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

For classification and regression using package kernlab with tuning parameters:

Sigma (sigma)

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

For classification and regression using package gam with tuning parameters:

Span (span)
Degree (degree)

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

For classification and regression using package mgcv with tuning parameters:

Feature Selection (select)
Method (method)

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

For classification and regression using package gam with tuning parameters:

Degrees of Freedom (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 MASS with no tuning parameters

Generalized Partial Least Squares (method = 'gpls')

For classification using package gpls with tuning parameters:

Number of Components (K.prov)

glmnet (method = 'glmnet')

For classification and regression using package glmnet with tuning parameters:

Mixing Percentage (alpha)
Regularization Parameter (lambda)

Greedy Prototype Selection (method = 'protoclass')

For classification using packages proxy and protoclass with tuning parameters:

Ball Size (eps)
Distance Order (Minkowski)

Heteroscedastic Discriminant Analysis (method = 'hda')

For classification using package hda with tuning parameters:

Gamma (gamma)
Lambda (lambda)
Dimension of the Discriminative Subspace (newdim)

High Dimensional Discriminant Analysis (method = 'hdda')

For classification using package HDclassif with tuning parameters:

Threshold (threshold)
Model Type (model)

Independent Component Regression (method = 'icr')

For regression using package fastICA with tuning parameters:

Number of Components (n.comp)

k-Nearest Neighbors (method = 'kknn')

For classification and regression using package kknn with tuning parameters:

Max. Number of Neighbors (kmax)
Distance (distance)
Kernel (kernel)

k-Nearest Neighbors (method = 'knn')

For classification and regression with tuning parameters:

Number of Neighbors (k)

Learning Vector Quantization (method = 'lvq')

For classification using package class with tuning parameters:

Codebook Size (size)
Number of Prototypes (k)

Least Angle Regression (method = 'lars')

For regression using package lars with tuning parameters:

Fraction (fraction)

Least Angle Regression (method = 'lars2')

For regression using package lars with tuning parameters:

Number of Steps (step)

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

For classification using package kernlab with no tuning parameters

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

For classification using package kernlab with tuning parameters:

Polynomial Degree (degree)
Scale (scale)

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

For classification using package kernlab with tuning parameters:

Sigma (sigma)

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)

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

For classification using packages klaR and MASS with tuning parameters:

Maximum Number of Variables (maxvar)
Search Direction (direction)

Linear Regression (method = 'lm')

For regression with no tuning parameters

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

For regression using package leaps with tuning parameters:

Maximum Number of Predictors (nvmax)

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

For regression using package leaps with tuning parameters:

Maximum Number of Predictors (nvmax)

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

For regression using package leaps with tuning parameters:

Maximum Number of Predictors (nvmax)

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

For regression using package MASS with no tuning parameters

Logic Regression (method = 'logreg')

For classification and regression using package LogicReg with tuning parameters:

Maximum Number of Leaves (treesize)
Number of Trees (ntrees)

Logistic Model Trees (method = 'LMT')

For classification using package RWeka with tuning parameters:

Number of Iteratons (iter)

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)

Model Averaged Neural Network (method = 'avNNet')

For classification and regression using package nnet with tuning parameters:

Number of Hidden Units (size)
Weight Decay (decay)
Bagging (bag)

Model Rules (method = 'M5Rules')

For regression using package RWeka with tuning parameters:

Pruned (pruned)
Smoothed (smoothed)

Model Tree (method = 'M5')

For regression using package RWeka with tuning parameters:

Pruned (pruned)
Smoothed (smoothed)
Rules (rules)

Multi-Layer Perceptron (method = 'mlp')

For classification and regression using package RSNNS with tuning parameters:

Number of Hidden Units (size)

Multi-Layer Perceptron (method = 'mlpWeightDecay')

For classification and regression using package RSNNS with tuning parameters:

Number of Hidden Units (size)
Weight Decay (decay)

Multivariate Adaptive Regression Spline (method = 'earth')

For classification and regression using package earth with tuning parameters:

Number of Terms (nprune)
Product Degree (degree)

Multivariate Adaptive Regression Splines (method = 'gcvEarth')

For classification and regression using package earth with tuning parameters:

Product Degree (degree)

Naive Bayes (method = 'nb')

For classification using package klaR with tuning parameters:

Laplace Correction (fL)
Distribution Type (usekernel)

Nearest Shrunken Centroids (method = 'pam')

For classification using package pamr with tuning parameters:

Shrinkage Threshold (threshold)

Neural Network (method = 'neuralnet')

For regression using package neuralnet with tuning parameters:

Number of Hidden Units in Layer 1 (layer1)
Number of Hidden Units in Layer 2 (layer2)
Number of Hidden Units in Layer 3 (layer3)

Neural Network (method = 'nnet')

For classification and regression using package nnet with tuning parameters:

Number of Hidden Units (size)
Weight Decay (decay)

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

For classification and regression using package nnet with tuning parameters:

Number of Hidden Units (size)
Weight Decay (decay)

Oblique Random Forest (method = 'ORFlog')

For classification using package obliqueRF with tuning parameters:

Number of Randomly Selected Predictors (mtry)

Oblique Random Forest (method = 'ORFpls')

For classification using package obliqueRF with tuning parameters:

Number of Randomly Selected Predictors (mtry)

Oblique Random Forest (method = 'ORFridge')

For classification using package obliqueRF with tuning parameters:

Number of Randomly Selected Predictors (mtry)

Oblique Random Forest (method = 'ORFsvm')

For classification using package obliqueRF with tuning parameters:

Number of Randomly Selected Predictors (mtry)

Oblique Trees (method = 'oblique.tree')

For classification using package oblique.tree with tuning parameters:

Oblique Splits (oblique.splits)
Variable Selection Method (variable.selection)

Parallel Random Forest (method = 'parRF')

For classification and regression using package randomForest with tuning parameters:

Number of Randomly Selected Predictors (mtry)

partDSA (method = 'partDSA')

For classification and regression using package partDSA with tuning parameters:

Number of Terminal Partitions (cut.off.growth)
Minimum Percent Difference (MPD)

Partial Least Squares (method = 'kernelpls')

For classification and regression using package pls with tuning parameters:

Number of Components (ncomp)

Partial Least Squares (method = 'pls')

For classification and regression using package pls with tuning parameters:

Number of Components (ncomp)

Partial Least Squares (method = 'simpls')

For classification and regression using package pls with tuning parameters:

Number of Components (ncomp)

Partial Least Squares (method = 'widekernelpls')

For classification and regression using package pls with tuning parameters:

Number of Components (ncomp)

Penalized Discriminant Analysis (method = 'pda')

For classification using package mda with tuning parameters:

Shrinkage Penalty Coefficient (lambda)

Penalized Discriminant Analysis (method = 'pda2')

For classification using package mda with tuning parameters:

Degrees of Freedom (df)

Penalized Linear Discriminant Analysis (method = 'PenalizedLDA')

For classification using packages penalizedLDA and plyr with tuning parameters:

L1 Penalty (lambda)
Number of Discriminant Functions (K)

Penalized Linear Regression (method = 'penalized')

For regression using package penalized with tuning parameters:

L1 Penalty (lambda1)
L2 Penalty (lambda2)

Penalized Logistic Regression (method = 'plr')

For classification using package stepPlr with tuning parameters:

L2 Penalty (lambda)
Complexity Parameter (cp)

Penalized Multinomial Regression (method = 'multinom')

For classification using package nnet with tuning parameters:

Weight Decay (decay)

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

For regression using package KRLS with tuning parameters:

Regularization Parameter (lambda)
Polynomial Degree (degree)

Principal Component Analysis (method = 'pcr')

For regression using package pls with tuning parameters:

Number of Components (ncomp)

Projection Pursuit Regression (method = 'ppr')

For regression with tuning parameters:

Number of Terms (nterms)

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)
Search Direction (direction)

Quantile Random Forest (method = 'qrf')

For regression using package quantregForest with tuning parameters:

Number of Randomly Selected Predictors (mtry)

Quantile Regression Neural Network (method = 'qrnn')

For regression using package qrnn with tuning parameters:

Number of Hidden Units (n.hidden)
Weight Decay (penalty)
Bagged Models? (bag)

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

For regression using packages KRLS and kernlab with tuning parameters:

Regularization Parameter (lambda)
Sigma (sigma)

Radial Basis Function Network (method = 'rbf')

For classification using package RSNNS with tuning parameters:

Number of Hidden Units (size)

Radial Basis Function Network (method = 'rbfDDA')

For classification and regression using package RSNNS with tuning parameters:

Activation Limit for Conflicting Classes (negativeThreshold)

Random Ferns (method = 'rFerns')

For classification using package rFerns with tuning parameters:

Fern Depth (depth)

Random Forest (method = 'rf')

For classification and regression using package randomForest with tuning parameters:

Number of Randomly Selected Predictors (mtry)

Random Forest by Randomization (method = 'extraTrees')

For classification and regression using package extraTrees with tuning parameters:

Number of Randomly Selected Predictors (mtry)
Number of Random Cuts (numRandomCuts)

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)

Random k-Nearest Neighbors (method = 'rknn')

For classification and regression using package rknn with tuning parameters:

Number of Neighbors (k)
Number of Randomly Selected Predictors (mtry)

Random k-Nearest Neighbors with Feature Selection (method = 'rknnBel')

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

Number of Neighbors (k)
Number of Randomly Selected Predictors (mtry)
Number of Features Dropped (d)

Regularized Discriminant Analysis (method = 'rda')

For classification using package klaR with tuning parameters:

Gamma (gamma)
Lambda (lambda)

Regularized Random Forest (method = 'RRF')

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

Number of Randomly Selected Predictors (mtry)
Regularization Value (coefReg)
Importance Coefficient (coefImp)

Regularized Random Forest (method = 'RRFglobal')

For classification and regression using package RRF with tuning parameters:

Number of Randomly Selected Predictors (mtry)
Regularization Value (coefReg)

Relaxed Lasso (method = 'relaxo')

For regression using packages relaxo and plyr with tuning parameters:

Penalty Parameter (lambda)
Relaxation Parameter (phi)

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)
Polynomial Degree (degree)

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

For regression using package kernlab with tuning parameters:

Sigma (sigma)

Ridge Regression (method = 'ridge')

For regression using package elasticnet with tuning parameters:

Weight Decay (lambda)

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

For regression using package foba with tuning parameters:

Number of Variables Retained (k)
L2 Penalty (lambda)

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 no tuning parameters

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)
Robustness Parameter (hp)
Penalty Type (penalty)

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)

Rule-Based Classifier (method = 'JRip')

For classification using package RWeka with tuning parameters:

Number of Optimizations (NumOpt)

Rule-Based Classifier (method = 'PART')

For classification using package RWeka with tuning parameters:

Confidence Threshold (threshold)
Confidence Threshold (pruned)

Self-Organizing Map (method = 'bdk')

For classification and regression using package kohonen with tuning parameters:

Row (xdim)
Columns (ydim)
X Weight (xweight)
Topology (topo)

Self-Organizing Maps (method = 'xyf')

For classification and regression using package kohonen with tuning parameters:

Row (xdim)
Columns (ydim)
X Weight (xweight)
Topology (topo)

Shrinkage Discriminant Analysis (method = 'sda')

For classification using package sda with tuning parameters:

Diagonalize (diagonal)
shrinkage (lambda)

SIMCA (method = 'CSimca')

For classification using package rrcovHD with no tuning parameters

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 Linear Discriminant Analysis (method = 'sparseLDA')

For classification using package sparseLDA with tuning parameters:

Number of Predictors (NumVars)
Lambda (lambda)

Sparse Mixture Discriminant Analysis (method = 'smda')

For classification using package sparseLDA with tuning parameters:

Number of Predictors (NumVars)
Lambda (lambda)
Number of Subclasses (R)

Sparse Partial Least Squares (method = 'spls')

For classification and regression using package spls with tuning parameters:

Number of Components (K)
Threshold (eta)
Kappa (kappa)

Stabilized Linear Discriminant Analysis (method = 'slda')

For classification using package ipred with no tuning parameters

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

For classification and regression using package deepnet with tuning parameters:

Hidden Layer 1 (layer1)
Hidden Layer 2 (layer2)
Hidden Layer 3 (layer3)
Hidden Dropouts (hidden_dropout)
Visible Dropout (visible_dropout)

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)
Max Tree Depth (interaction.depth)
Shrinkage (shrinkage)

Supervised Principal Component Analysis (method = 'superpc')

For regression using package superpc with tuning parameters:

Threshold (threshold)
Number of Components (n.components)

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

For classification using package kernlab with tuning parameters:

Sigma (sigma)
Cost (C)
Weight (Weight)

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

For classification and regression using package kernlab with tuning parameters:

Cost (C)

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

For classification and regression using package kernlab with tuning parameters:

Polynomial Degree (degree)
Cost (scale)
Scale (C)

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

For classification and regression using package kernlab with tuning parameters:

Sigma (sigma)
Cost (C)

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

For classification and regression using package kernlab with tuning parameters:

Cost (C)

The lasso (method = 'lasso')

For regression using package elasticnet with tuning parameters:

Fraction of Full Solution (fraction)

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

For classification and regression using package evtree with tuning parameters:

Complexity Parameter (alpha)

Tree-Based Ensembles (method = 'nodeHarvest')

For classification and regression using package nodeHarvest with tuning parameters:

Maximum Interaction Depth (maxinter)
Prediction Mode (mode)

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

For classification using package vbmp with tuning parameters:

Theta Estimated (estimateTheta)

Arguments

References

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