models: A List of Available Models in train

Description References

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:

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

For classification using packages adabag and plyr with tuning parameters:

Adaptive Mixture Discriminant Analysis (method = 'amdai')

For classification using package adaptDA with tuning parameters:

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

For regression using package frbs with tuning parameters:

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

For classification using package VGAM with tuning parameters:

Bagged AdaBoost (method = 'AdaBag')

For classification using packages adabag and plyr with tuning parameters:

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:

Note: Unlike other packages used by train, the earth package is fully loaded when this model is used.

Bagged Flexible Discriminant Analysis (method = 'bagFDA')

For classification using packages earth and mda with tuning parameters:

Note: Unlike other packages used by train, the earth package is fully loaded when this model is used.

Bagged Logic Regression (method = 'logicBag')

For classification and regression using package logicFS with tuning parameters:

Note: Unlike other packages used by train, the logicFS package is fully loaded when this model is used.

Bagged MARS (method = 'bagEarth')

For classification and regression using package earth with tuning parameters:

Note: Unlike other packages used by train, the earth package is fully loaded when this model is used.

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

For classification and regression using package earth with tuning parameters:

Note: Unlike other packages used by train, the earth package is fully loaded when this model is used.

Bagged Model (method = 'bag')

For classification and regression using package caret with tuning parameters:

Bayesian Additive Regression Trees (method = 'bartMachine')

For classification and regression using package bartMachine with tuning parameters:

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:

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.

Note: This model makes predictions by averaging the predictions based on the posterior estimates of the regression coefficients. While it is possible that some of these posterior estimates are zero for non-informative predictors, the final predicted value may be a function of many (or even all) predictors.

Binary Discriminant Analysis (method = 'binda')

For classification using package binda with tuning parameters:

Boosted Classification Trees (method = 'ada')

For classification using packages ada and plyr with tuning parameters:

Boosted Generalized Additive Model (method = 'gamboost')

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

Note: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Boosted Generalized Linear Model (method = 'glmboost')

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

Note: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Boosted Linear Model (method = 'BstLm')

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

Boosted Logistic Regression (method = 'LogitBoost')

For classification using package caTools with tuning parameters:

Boosted Smoothing Spline (method = 'bstSm')

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

Boosted Tree (method = 'blackboost')

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

Boosted Tree (method = 'bstTree')

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

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

For classification using package RWeka with tuning parameters:

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

For classification using packages C50 and plyr with tuning parameters:

CART (method = 'rpart')

For classification and regression using package rpart with tuning parameters:

CART (method = 'rpart1SE')

For classification and regression using package rpart with no tuning parameters.

Note: This CART model replicates the same process used by the rpart function where the model complexity is determined using the one-standard error method. This procedure is replicated inside of the resampling done by train so that an external resampling estimate can be obtained.

CART (method = 'rpart2')

For classification and regression using package rpart with tuning parameters:

CART or Ordinal Responses (method = 'rpartScore')

For classification using packages rpartScore and plyr with tuning parameters:

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

For classification using package CHAID with tuning parameters:

Conditional Inference Random Forest (method = 'cforest')

For classification and regression using package party with tuning parameters:

Conditional Inference Tree (method = 'ctree')

For classification and regression using package party with tuning parameters:

Conditional Inference Tree (method = 'ctree2')

For classification and regression using package party with tuning parameters:

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

For classification using package VGAM with tuning parameters:

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

For classification using packages C50 and plyr with tuning parameters:

Cost-Sensitive CART (method = 'rpartCost')

For classification using packages rpart and plyr with tuning parameters:

Cubist (method = 'cubist')

For regression using package Cubist with tuning parameters:

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

For classification using package VGAM with tuning parameters:

DeepBoost (method = 'deepboost')

For classification using package deepboost with tuning parameters:

Diagonal Discriminant Analysis (method = 'dda')

For classification using package sparsediscrim with tuning parameters:

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

For classification using package kerndwd with tuning parameters:

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

For classification using packages kernlab and kerndwd with tuning parameters:

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

For regression using package frbs with tuning parameters:

Elasticnet (method = 'enet')

For regression using package elasticnet with tuning parameters:

Ensembles of Generalized Linear Models (method = 'randomGLM')

For classification and regression using package randomGLM with tuning parameters:

Note: Unlike other packages used by train, the randomGLM package is fully loaded when this model is used.

eXtreme Gradient Boosting (method = 'xgbDART')

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

eXtreme Gradient Boosting (method = 'xgbLinear')

For classification and regression using package xgboost with tuning parameters:

eXtreme Gradient Boosting (method = 'xgbTree')

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

Extreme Learning Machine (method = 'elm')

For classification and regression using package elmNN with tuning parameters:

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

For classification using package HiDimDA with tuning parameters:

Flexible Discriminant Analysis (method = 'fda')

For classification using packages earth and mda with tuning parameters:

Note: Unlike other packages used by train, the earth package is fully loaded when this model is used.

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

For regression using package frbs with tuning parameters:

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

For classification using package frbs with tuning parameters:

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

For classification using package frbs with tuning parameters:

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

For classification using package frbs with tuning parameters:

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

For regression using package frbs with tuning parameters:

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

For regression using package frbs with tuning parameters:

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

For classification using package frbs with tuning parameters:

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:

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

For classification and regression using package kernlab with tuning parameters:

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

For classification and regression using package gam with tuning parameters:

Note: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion. Unlike other packages used by train, the gam package is fully loaded when this model is used.

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

For classification and regression using package mgcv with tuning parameters:

Note: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion. Unlike other packages used by train, the mgcv package is fully loaded when this model is used.

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

For classification and regression using package mgcv with tuning parameters:

Note: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion. Unlike other packages used by train, the mgcv package is fully loaded when this model is used.

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

For classification and regression using package gam with tuning parameters:

Note: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion. Unlike other packages used by train, the gam package is fully loaded when this model is used.

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:

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

For regression using package frbs with tuning parameters:

glmnet (method = 'glmnet_h2o')

For classification and regression using package h2o with tuning parameters:

glmnet (method = 'glmnet')

For classification and regression using packages glmnet and Matrix with tuning parameters:

Gradient Boosting Machines (method = 'gbm_h2o')

For classification and regression using package h2o with tuning parameters:

Greedy Prototype Selection (method = 'protoclass')

For classification using packages proxy and protoclass with tuning parameters:

Heteroscedastic Discriminant Analysis (method = 'hda')

For classification using package hda with tuning parameters:

High Dimensional Discriminant Analysis (method = 'hdda')

For classification using package HDclassif with tuning parameters:

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

For classification using package sparsediscrim with tuning parameters:

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

For regression using package frbs with tuning parameters:

Independent Component Regression (method = 'icr')

For regression using package fastICA with tuning parameters:

k-Nearest Neighbors (method = 'kknn')

For classification and regression using package kknn with tuning parameters:

k-Nearest Neighbors (method = 'knn')

For classification and regression with tuning parameters:

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

For classification using package LiblineaR with tuning parameters:

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

For classification and regression using package LiblineaR with tuning parameters:

Learning Vector Quantization (method = 'lvq')

For classification using package class with tuning parameters:

Least Angle Regression (method = 'lars')

For regression using package lars with tuning parameters:

Least Angle Regression (method = 'lars2')

For regression using package lars with tuning parameters:

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

For classification using package kernlab with tuning parameters:

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

For classification using package kernlab with tuning parameters:

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

For classification using package kernlab with tuning parameters:

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:

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

For classification using packages klaR and MASS with tuning parameters:

Linear Distance Weighted Discrimination (method = 'dwdLinear')

For classification using package kerndwd with tuning parameters:

Linear Regression (method = 'lm')

For regression with tuning parameters:

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

For regression using package leaps with tuning parameters:

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

For regression using package leaps with tuning parameters:

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

For regression using package leaps with tuning parameters:

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:

Localized Linear Discriminant Analysis (method = 'loclda')

For classification using package klaR with tuning parameters:

Logic Regression (method = 'logreg')

For classification and regression using package LogicReg with tuning parameters:

Logistic Model Trees (method = 'LMT')

For classification using package RWeka with tuning parameters:

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:

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

For classification using package bnclassify with tuning parameters:

Model Averaged Neural Network (method = 'avNNet')

For classification and regression using package nnet with tuning parameters:

Model Rules (method = 'M5Rules')

For regression using package RWeka with tuning parameters:

Model Tree (method = 'M5')

For regression using package RWeka with tuning parameters:

Monotone Multi-Layer Perceptron Neural Network (method = 'monmlp')

For classification and regression using package monmlp with tuning parameters:

Multi-Layer Perceptron (method = 'mlp')

For classification and regression using package RSNNS with tuning parameters:

Multi-Layer Perceptron (method = 'mlpWeightDecay')

For classification and regression using package RSNNS with tuning parameters:

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

For classification and regression using package RSNNS with tuning parameters:

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

For classification and regression using package RSNNS with tuning parameters:

Multi-Step Adaptive MCP-Net (method = 'msaenet')

For classification and regression using package msaenet with tuning parameters:

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

For classification and regression using packages FCNN4R and plyr with tuning parameters:

Multilayer Perceptron Network with Dropout (method = 'mlpKerasDropout')

For classification and regression using package keras with tuning parameters:

Note: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Unlike other packages used by train, the dplyr package is fully loaded when this model is used.

Multilayer Perceptron Network with Dropout (method = 'mlpKerasDropoutCost')

For classification using package keras with tuning parameters:

Note: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Finally, the cost parameter weights the first class in the outcome vector. Unlike other packages used by train, the dplyr package is fully loaded when this model is used.

Multilayer Perceptron Network with Weight Decay (method = 'mlpKerasDecay')

For classification and regression using package keras with tuning parameters:

Note: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Unlike other packages used by train, the dplyr package is fully loaded when this model is used.

Multilayer Perceptron Network with Weight Decay (method = 'mlpKerasDecayCost')

For classification using package keras with tuning parameters:

Note: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Finally, the cost parameter weights the first class in the outcome vector. Unlike other packages used by train, the dplyr package is fully loaded when this model is used.

Multivariate Adaptive Regression Spline (method = 'earth')

For classification and regression using package earth with tuning parameters:

Note: Unlike other packages used by train, the earth package is fully loaded when this model is used.

Multivariate Adaptive Regression Splines (method = 'gcvEarth')

For classification and regression using package earth with tuning parameters:

Note: Unlike other packages used by train, the earth package is fully loaded when this model is used.

Naive Bayes (method = 'naive_bayes')

For classification using package naivebayes with tuning parameters:

Naive Bayes (method = 'nb')

For classification using package klaR with tuning parameters:

Naive Bayes Classifier (method = 'nbDiscrete')

For classification using package bnclassify with tuning parameters:

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

For classification using package bnclassify with tuning parameters:

Nearest Shrunken Centroids (method = 'pam')

For classification using package pamr with tuning parameters:

Negative Binomial Generalized Linear Model (method = 'glm.nb')

For regression using package MASS with tuning parameters:

Neural Network (method = 'mxnet')

For classification and regression using package mxnet with tuning parameters:

Note: The mxnet package is not yet on CRAN. See http://mxnet.io for installation instructions.

Neural Network (method = 'mxnetAdam')

For classification and regression using package mxnet with tuning parameters:

Note: The mxnet package is not yet on CRAN. See http://mxnet.io for installation instructions. Users are strongly advised to define num.round themselves.

Neural Network (method = 'neuralnet')

For regression using package neuralnet with tuning parameters:

Neural Network (method = 'nnet')

For classification and regression using package nnet with tuning parameters:

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

For classification and regression using package nnet with tuning parameters:

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

For regression using package rqPen with tuning parameters:

Non-Informative Model (method = 'null')

For classification and regression with no tuning parameters.

Note: Since this model always predicts the same value, R-squared values will always be estimated to be NA.

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:

Note: Unlike other packages used by train, the obliqueRF package is fully loaded when this model is used.

Oblique Random Forest (method = 'ORFpls')

For classification using package obliqueRF with tuning parameters:

Note: Unlike other packages used by train, the obliqueRF package is fully loaded when this model is used.

Oblique Random Forest (method = 'ORFridge')

For classification using package obliqueRF with tuning parameters:

Note: Unlike other packages used by train, the obliqueRF package is fully loaded when this model is used.

Oblique Random Forest (method = 'ORFsvm')

For classification using package obliqueRF with tuning parameters:

Note: Unlike other packages used by train, the obliqueRF package is fully loaded when this model is used.

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

For classification using package snn with tuning parameters:

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

For classification using package MASS with tuning parameters:

Parallel Random Forest (method = 'parRF')

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

partDSA (method = 'partDSA')

For classification and regression using package partDSA with tuning parameters:

Partial Least Squares (method = 'kernelpls')

For classification and regression using package pls with tuning parameters:

Partial Least Squares (method = 'pls')

For classification and regression using package pls with tuning parameters:

Partial Least Squares (method = 'simpls')

For classification and regression using package pls with tuning parameters:

Partial Least Squares (method = 'widekernelpls')

For classification and regression using package pls with tuning parameters:

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

For classification and regression using package plsRglm with tuning parameters:

Note: Unlike other packages used by train, the plsRglm package is fully loaded when this model is used.

Patient Rule Induction Method (method = 'PRIM')

For classification using package supervisedPRIM with tuning parameters:

Penalized Discriminant Analysis (method = 'pda')

For classification using package mda with tuning parameters:

Penalized Discriminant Analysis (method = 'pda2')

For classification using package mda with tuning parameters:

Penalized Linear Discriminant Analysis (method = 'PenalizedLDA')

For classification using packages penalizedLDA and plyr with tuning parameters:

Penalized Linear Regression (method = 'penalized')

For regression using package penalized with tuning parameters:

Penalized Logistic Regression (method = 'plr')

For classification using package stepPlr with tuning parameters:

Penalized Multinomial Regression (method = 'multinom')

For classification using package nnet with tuning parameters:

Penalized Ordinal Regression (method = 'ordinalNet')

For classification using packages ordinalNet and plyr with tuning parameters:

Note: Requires ordinalNet package version >= 2.0

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

For regression using package KRLS with tuning parameters:

Principal Component Analysis (method = 'pcr')

For regression using package pls with tuning parameters:

Projection Pursuit Regression (method = 'ppr')

For regression with tuning parameters:

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:

Quantile Random Forest (method = 'qrf')

For regression using package quantregForest with tuning parameters:

Quantile Regression Neural Network (method = 'qrnn')

For regression using package qrnn with tuning parameters:

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

For regression using package rqPen with tuning parameters:

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

For regression using packages KRLS and kernlab with tuning parameters:

Radial Basis Function Network (method = 'rbf')

For classification and regression using package RSNNS with tuning parameters:

Radial Basis Function Network (method = 'rbfDDA')

For classification and regression using package RSNNS with tuning parameters:

Random Ferns (method = 'rFerns')

For classification using package rFerns with tuning parameters:

Random Forest (method = 'ranger')

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

Random Forest (method = 'Rborist')

For classification and regression using package Rborist with tuning parameters:

Random Forest (method = 'rf')

For classification and regression using package randomForest with tuning parameters:

Random Forest by Randomization (method = 'extraTrees')

For classification and regression using package extraTrees with tuning parameters:

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

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

Regularized Discriminant Analysis (method = 'rda')

For classification using package klaR with tuning parameters:

Regularized Linear Discriminant Analysis (method = 'rlda')

For classification using package sparsediscrim with tuning parameters:

Regularized Logistic Regression (method = 'regLogistic')

For classification using package LiblineaR with tuning parameters:

Regularized Random Forest (method = 'RRF')

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

Regularized Random Forest (method = 'RRFglobal')

For classification and regression using package RRF with tuning parameters:

Relaxed Lasso (method = 'relaxo')

For regression using packages relaxo and plyr with tuning parameters:

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:

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

For regression using package kernlab with tuning parameters:

Ridge Regression (method = 'ridge')

For regression using package elasticnet with tuning parameters:

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

For regression using package foba with tuning parameters:

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:

Robust Mixture Discriminant Analysis (method = 'rmda')

For classification using package robustDA with 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:

Note: Unlike other packages used by train, the rrlda package is fully loaded when this model is used.

Robust SIMCA (method = 'RSimca')

For classification using package rrcovHD with no tuning parameters.

Note: Unlike other packages used by train, the rrcovHD package is fully loaded when this model is used.

ROC-Based Classifier (method = 'rocc')

For classification using package rocc with tuning parameters:

Rotation Forest (method = 'rotationForest')

For classification using package rotationForest with tuning parameters:

Rotation Forest (method = 'rotationForestCp')

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

Rule-Based Classifier (method = 'JRip')

For classification using package RWeka with tuning parameters:

Rule-Based Classifier (method = 'PART')

For classification using package RWeka with tuning parameters:

Self-Organizing Maps (method = 'xyf')

For classification and regression using package kohonen with tuning parameters:

Note: As of version 3.0.0 of the kohonen package, the argument user.weights replaces the old alpha parameter. user.weights is usually a vector of relative weights such as c(1, 3) but is parameterized here as a proportion such as c(1-.75, .75) where the .75 is the value of the tuning parameter passed to train and indicates that the outcome layer has 3 times the weight as the predictor layer.

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

For classification using package bnclassify with tuning parameters:

Shrinkage Discriminant Analysis (method = 'sda')

For classification using package sda with tuning parameters:

SIMCA (method = 'CSimca')

For classification using packages rrcov and rrcovHD with no tuning parameters.

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

For regression using package frbs with 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 Distance Weighted Discrimination (method = 'sdwd')

For classification using package sdwd with tuning parameters:

Sparse Linear Discriminant Analysis (method = 'sparseLDA')

For classification using package sparseLDA with tuning parameters:

Sparse Mixture Discriminant Analysis (method = 'smda')

For classification using package sparseLDA with tuning parameters:

Sparse Partial Least Squares (method = 'spls')

For classification and regression using package spls with tuning parameters:

Spike and Slab Regression (method = 'spikeslab')

For regression using packages spikeslab and plyr with tuning parameters:

Note: Unlike other packages used by train, the spikeslab package is fully loaded when this model is used.

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:

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

For classification and regression using package deepnet with tuning parameters:

Stochastic Gradient Boosting (method = 'gbm')

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

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

For regression using package frbs with tuning parameters:

Supervised Principal Component Analysis (method = 'superpc')

For regression using package superpc with tuning parameters:

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

For classification and regression using package kernlab with tuning parameters:

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

For classification using package kernlab with tuning parameters:

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

For classification and regression using package kernlab with tuning parameters:

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

For classification and regression using package kernlab with tuning parameters:

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

For classification and regression using package e1071 with tuning parameters:

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

For classification and regression using package kernlab with tuning parameters:

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

For classification and regression using package kernlab with tuning parameters:

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

For classification and regression using package kernlab with tuning parameters:

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

For classification and regression using package kernlab with tuning parameters:

Note: This SVM model tunes over the cost parameter and the RBF kernel parameter sigma. In the latter case, using tuneLength will, at most, evaluate six values of the kernel parameter. This enables a broad search over the cost parameter and a relatively narrow search over sigma

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

For classification and regression using package kernlab with tuning parameters:

The Bayesian lasso (method = 'blasso')

For regression using package monomvn with tuning parameters:

Note: This model creates predictions using the mean of the posterior distributions but sets some parameters specifically to zero based on the tuning parameter sparsity. For example, when sparsity = .5, only coefficients where at least half the posterior estimates are nonzero are used.

The lasso (method = 'lasso')

For regression using package elasticnet with tuning parameters:

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

For classification using package bnclassify with tuning parameters:

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

For classification using package bnclassify with tuning parameters:

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

For classification using package bnclassify with tuning parameters:

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

For classification and regression using package evtree with tuning parameters:

Tree-Based Ensembles (method = 'nodeHarvest')

For classification and regression using package nodeHarvest with tuning parameters:

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

For classification using package vbmp with tuning parameters:

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

For regression using package frbs with tuning parameters:

Weighted Subspace Random Forest (method = 'wsrf')

For classification using package wsrf with tuning parameters:

References

“Using your own model in train” (https://topepo.github.io/caret/using-your-own-model-in-train.html)


caret documentation built on May 2, 2019, 5:39 a.m.