train_model_list

0th

Percentile

A List of Available Models in train

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

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

For classification using package adabag 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)

Bagged AdaBoost (method = 'AdaBag')

For classification using package adabag with tuning parameters:

  • Number of Trees (mfinal, numeric)
  • Max Tree Depth (maxdepth, numeric)

Bagged CART (method = 'treebag')

For classification and regression using packages ipred and plyr 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 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)

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 package ada 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 package mboost with tuning parameters:

  • Number of Boosting Iterations (mstop, numeric)
  • AIC Prune? (prune, character)

Boosted Generalized Linear Model (method = 'glmboost')

For classification and regression using package mboost with tuning parameters:

  • Number of Boosting Iterations (mstop, numeric)
  • AIC Prune? (prune, character)

Boosted Linear Model (method = 'bstLs')

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 = 'rpart2')

For classification and regression using package rpart with tuning parameters:

  • Max Tree Depth (maxdepth, 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)

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)

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)

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.MOGAL')

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 = '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)

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)

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 no tuning parameters

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)

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

For classification using package kernlab with tuning parameters:

  • Sigma (sigma, 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 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, 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

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 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)

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)

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)

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)

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

For classification using package MASS with no tuning parameters

Parallel Random Forest (method = 'parRF')

For classification and regression using package randomForest 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)

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)

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 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 = '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 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)

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

For classification and regression using package rknn with tuning parameters:

  • Number of Neighbors (k, numeric)
  • Number of Randomly Selected Predictors (mtry, numeric)

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, numeric)
  • Number of Randomly Selected Predictors (mtry, numeric)
  • Number of Features Dropped (d, numeric)

Regularized Discriminant Analysis (method = 'rda')

For classification using package klaR with tuning parameters:

  • Gamma (gamma, numeric)
  • Lambda (lambda, numeric)

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 no tuning parameters

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)

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)

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 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)

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, 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)

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 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 Spectrum String Kernel (method = 'svmSpectrumString')

For classification and regression using package kernlab with tuning parameters:

  • length (length, numeric)
  • Cost (C, numeric)

The lasso (method = 'lasso')

For regression using package elasticnet with tuning parameters:

  • Fraction of Full Solution (fraction, 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)

Keywords
models
References

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

Aliases
  • train_model_list
  • models
Documentation reproduced from package caret, version 6.0-41, License: GPL (>= 2)

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