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 Classification Trees (method = 'adaboost')

For classification using package fastAdaboost with tuning parameters:

  • Number of Trees (nIter, numeric)
  • Method (method, character)

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

For classification using packages adabag and plyr with tuning parameters:

  • Number of Trees (mfinal, numeric)
  • Max Tree Depth (maxdepth, numeric)
  • Coefficient Type (coeflearn, character)

Adaptive Mixture Discriminant Analysis (method = 'amdai')

For classification using package adaptDA with tuning parameters:

  • Model Type (model, character)

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

For regression using package frbs with tuning parameters:

  • Number of Fuzzy Terms (num.labels, numeric)
  • Max. Iterations (max.iter, numeric)

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

For classification using package VGAM with tuning parameters:

  • Parallel Curves (parallel, logical)
  • Link Function (link, character)

Bagged AdaBoost (method = 'AdaBag')

For classification using packages adabag and plyr with tuning parameters:

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

Bagged CART (method = 'treebag')

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

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

For classification using package earth with tuning parameters:

  • Product Degree (degree, numeric)

Bagged Flexible Discriminant Analysis (method = 'bagFDA')

For classification using packages earth and mda with tuning parameters:

  • Product Degree (degree, numeric)
  • Number of Terms (nprune, numeric)

Bagged Logic Regression (method = 'logicBag')

For classification and regression using package logicFS with tuning parameters:

  • Maximum Number of Leaves (nleaves, numeric)
  • Number of Trees (ntrees, numeric)

Bagged MARS (method = 'bagEarth')

For classification and regression using package earth with tuning parameters:

  • Number of Terms (nprune, numeric)
  • Product Degree (degree, numeric)

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

For classification and regression using package earth with tuning parameters:

  • Product Degree (degree, numeric)

Bagged Model (method = 'bag')

For classification and regression using package caret with tuning parameters:

  • Number of Randomly Selected Predictors (vars, numeric)

Bayesian Additive Regression Trees (method = 'bartMachine')

For classification and regression using package bartMachine with tuning parameters:

  • Number of Trees (num_trees, numeric)
  • Prior Boundary (k, numeric)
  • Base Terminal Node Hyperparameter (alpha, numeric)
  • Power Terminal Node Hyperparameter (beta, numeric)
  • Degrees of Freedom (nu, numeric)

Bayesian Generalized Linear Model (method = 'bayesglm')

For classification and regression using package arm with no tuning parameters

Bayesian Regularized Neural Networks (method = 'brnn')

For regression using package brnn with tuning parameters:

  • Number of Neurons (neurons, numeric)

Bayesian Ridge Regression (method = 'bridge')

For regression using package monomvn with no tuning parameters

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

For regression using package monomvn with no tuning parameters

Binary Discriminant Analysis (method = 'binda')

For classification using package binda with tuning parameters:

  • Shrinkage Intensity (lambda.freqs, numeric)

Boosted Classification Trees (method = 'ada')

For classification using packages ada and plyr with tuning parameters:

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

Boosted Generalized Additive Model (method = 'gamboost')

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

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

Boosted Generalized Linear Model (method = 'glmboost')

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

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

Boosted Linear Model (method = 'BstLm')

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

  • Number of Boosting Iterations (mstop, numeric)
  • Shrinkage (nu, numeric)

Boosted Logistic Regression (method = 'LogitBoost')

For classification using package caTools with tuning parameters:

  • Number of Boosting Iterations (nIter, numeric)

Boosted Smoothing Spline (method = 'bstSm')

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

  • Number of Boosting Iterations (mstop, numeric)
  • Shrinkage (nu, numeric)

Boosted Tree (method = 'blackboost')

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

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

Boosted Tree (method = 'bstTree')

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

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

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

For classification using package RWeka with tuning parameters:

  • Confidence Threshold (C, numeric)

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

For classification using packages C50 and plyr with tuning parameters:

  • Number of Boosting Iterations (trials, numeric)
  • Model Type (model, character)
  • Winnow (winnow, logical)

CART (method = 'rpart')

For classification and regression using package rpart with tuning parameters:

  • Complexity Parameter (cp, numeric)

CART (method = 'rpart1SE')

For classification and regression using package rpart with no tuning parameters

CART (method = 'rpart2')

For classification and regression using package rpart with tuning parameters:

  • Max Tree Depth (maxdepth, numeric)

CART or Ordinal Responses (method = 'rpartScore')

For classification using packages rpartScore and plyr with tuning parameters:

  • Complexity Parameter (cp, numeric)
  • Split Function (split, character)
  • Pruning Measure (prune, character)

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

For classification using package CHAID with tuning parameters:

  • Merging Threshold (alpha2, numeric)
  • Splitting former Merged Threshold (alpha3, numeric)
  • Splitting former Merged Threshold (alpha4, numeric)

Conditional Inference Random Forest (method = 'cforest')

For classification and regression using package party with tuning parameters:

  • Number of Randomly Selected Predictors (mtry, numeric)

Conditional Inference Tree (method = 'ctree')

For classification and regression using package party with tuning parameters:

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

Conditional Inference Tree (method = 'ctree2')

For classification and regression using package party with tuning parameters:

  • Max Tree Depth (maxdepth, numeric)
  • 1 - P-Value Threshold (mincriterion, numeric)

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

For classification using package VGAM with tuning parameters:

  • Parallel Curves (parallel, logical)
  • Link Function (link, character)

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

For classification using packages C50 and plyr with tuning parameters:

  • Number of Boosting Iterations (trials, numeric)
  • Model Type (model, character)
  • Winnow (winnow, logical)
  • Cost (cost, numeric)

Cost-Sensitive CART (method = 'rpartCost')

For classification using package rpart with tuning parameters:

  • Complexity Parameter (cp, numeric)
  • Cost (Cost, numeric)

Cubist (method = 'cubist')

For regression using package Cubist with tuning parameters:

  • Number of Committees (committees, numeric)
  • Number of Instances (neighbors, numeric)

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

For classification using package VGAM with tuning parameters:

  • Parallel Curves (parallel, logical)
  • Link Function (link, character)

DeepBoost (method = 'deepboost')

For classification using package deepboost with tuning parameters:

  • Number of Boosting Iterations (num_iter, numeric)
  • Tree Depth (tree_depth, numeric)
  • L1 Regularization (beta, numeric)
  • Tree Depth Regularization (lambda, numeric)
  • Loss (loss_type, character)

Diagonal Discriminant Analysis (method = 'dda')

For classification using package sparsediscrim with tuning parameters:

  • Model (model, character)
  • Shrinkage Type (shrinkage, character)

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

For classification using package kerndwd with tuning parameters:

  • Regularization Parameter (lambda, numeric)
  • q (qval, numeric)
  • Polynomial Degree (degree, numeric)
  • Scale (scale, numeric)

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

For classification using packages kernlab and kerndwd with tuning parameters:

  • Regularization Parameter (lambda, numeric)
  • q (qval, numeric)
  • Sigma (sigma, numeric)

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

For regression using package frbs with tuning parameters:

  • Threshold (Dthr, numeric)
  • Max. Iterations (max.iter, numeric)

Elasticnet (method = 'enet')

For regression using package elasticnet with tuning parameters:

  • Fraction of Full Solution (fraction, numeric)
  • Weight Decay (lambda, numeric)

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

For regression using package enpls with tuning parameters:

  • Max. Number of Components (maxcomp, numeric)

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

For regression using package enpls with tuning parameters:

  • Max. Number of Components (maxcomp, numeric)
  • Importance Cutoff (threshold, numeric)

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

For classification and regression using package randomGLM with tuning parameters:

  • Interaction Order (maxInteractionOrder, numeric)

eXtreme Gradient Boosting (method = 'xgbLinear')

For classification and regression using package xgboost with tuning parameters:

  • Number of Boosting Iterations (nrounds, numeric)
  • L2 Regularization (lambda, numeric)
  • L1 Regularization (alpha, numeric)
  • Learning Rate (eta, numeric)

eXtreme Gradient Boosting (method = 'xgbTree')

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

  • Number of Boosting Iterations (nrounds, numeric)
  • Max Tree Depth (max_depth, numeric)
  • Shrinkage (eta, numeric)
  • Minimum Loss Reduction (gamma, numeric)
  • Subsample Ratio of Columns (colsample_bytree, numeric)
  • Minimum Sum of Instance Weight (min_child_weight, numeric)

Extreme Learning Machine (method = 'elm')

For classification and regression using package elmNN with tuning parameters:

  • Number of Hidden Units (nhid, numeric)
  • Activation Function (actfun, character)

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

For classification using package HiDimDA with tuning parameters:

  • Number of Factors (q, numeric)

Flexible Discriminant Analysis (method = 'fda')

For classification using packages earth and mda with tuning parameters:

  • Product Degree (degree, numeric)
  • Number of Terms (nprune, numeric)

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

For regression using package frbs with tuning parameters:

  • Number of Fuzzy Terms (num.labels, numeric)
  • Max. Iterations (max.iter, numeric)

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

For classification using package frbs with tuning parameters:

  • Number of Fuzzy Terms (num.labels, numeric)
  • Membership Function (type.mf, character)

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

For classification using package frbs with tuning parameters:

  • Number of Fuzzy Terms (num.labels, numeric)
  • Population Size (popu.size, numeric)
  • Max. Generations (max.gen, numeric)

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

For classification using package frbs with tuning parameters:

  • Max. Number of Rules (max.num.rule, numeric)
  • Population Size (popu.size, numeric)
  • Max. Generations (max.gen, numeric)

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

For classification using package frbs with tuning parameters:

  • Number of Fuzzy Terms (num.labels, numeric)
  • Max. Iterations (max.iter, numeric)
  • Max. Generations (max.gen, numeric)

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

For regression using package frbs with tuning parameters:

  • Max. Generations (max.gen, numeric)
  • Max. Iterations (max.iter, numeric)
  • Max. Tuning Iterations (max.tune, numeric)

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

For regression using package frbs with tuning parameters:

  • Population Size (popu.size, numeric)
  • Number of Fuzzy Labels (num.labels, numeric)
  • Max. Generations (max.gen, numeric)

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

For classification using package frbs with tuning parameters:

  • Number of Fuzzy Terms (num.labels, numeric)
  • Membership Function (type.mf, character)

Gaussian Process (method = 'gaussprLinear')

For classification and regression using package kernlab with no tuning parameters

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

For classification and regression using package kernlab with tuning parameters:

  • Polynomial Degree (degree, numeric)
  • Scale (scale, numeric)

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

For classification and regression using package kernlab with tuning parameters:

  • Sigma (sigma, numeric)

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

For classification and regression using package gam with tuning parameters:

  • Span (span, numeric)
  • Degree (degree, numeric)

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

For classification and regression using package mgcv with tuning parameters:

  • Feature Selection (select, logical)
  • Method (method, character)

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

For classification and regression using package gam with tuning parameters:

  • Degrees of Freedom (df, numeric)

Generalized Linear Model (method = 'glm')

For classification and regression with no tuning parameters

Generalized Linear Model with Stepwise Feature Selection (method = 'glmStepAIC')

For classification and regression using package MASS with no tuning parameters

Generalized Partial Least Squares (method = 'gpls')

For classification using package gpls with tuning parameters:

  • Number of Components (K.prov, numeric)

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

For regression using package frbs with tuning parameters:

  • Population Size (popu.size, numeric)
  • Number of Fuzzy Labels (num.labels, numeric)
  • Max. Generations (max.gen, numeric)

glmnet (method = 'glmnet')

For classification and regression using package glmnet with tuning parameters:

  • Mixing Percentage (alpha, numeric)
  • Regularization Parameter (lambda, numeric)

Greedy Prototype Selection (method = 'protoclass')

For classification using packages proxy and protoclass with tuning parameters:

  • Ball Size (eps, numeric)
  • Distance Order (Minkowski, numeric)

Heteroscedastic Discriminant Analysis (method = 'hda')

For classification using package hda with tuning parameters:

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

High Dimensional Discriminant Analysis (method = 'hdda')

For classification using package HDclassif with tuning parameters:

  • Threshold (threshold, character)
  • Model Type (model, numeric)

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

For classification using package sparsediscrim with tuning parameters:

  • Gamma (gamma, numeric)
  • Lambda (lambda, numeric)
  • Shrinkage Type (shrinkage_type, character)

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

For regression using package frbs with tuning parameters:

  • Number of Fuzzy Terms (num.labels, numeric)
  • Max. Iterations (max.iter, numeric)

Independent Component Regression (method = 'icr')

For regression using package fastICA with tuning parameters:

  • Number of Components (n.comp, numeric)

k-Nearest Neighbors (method = 'kknn')

For classification and regression using package kknn with tuning parameters:

  • Max. Number of Neighbors (kmax, numeric)
  • Distance (distance, numeric)
  • Kernel (kernel, character)

k-Nearest Neighbors (method = 'knn')

For classification and regression with tuning parameters:

  • Number of Neighbors (k, numeric)

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

For regression using package rPython with tuning parameters:

  • Number of Neighbors (n_neighbors, numeric)
  • Weight Function (weights, character)
  • Algorithm (algorithm, character)
  • Leaf Size (leaf_size, numeric)
  • Distance Metric (metric, character)
  • p (p, numeric)

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 Distance Weighted Discrimination (method = 'dwdLinear')

For classification using package kerndwd with tuning parameters:

  • Regularization Parameter (lambda, numeric)
  • q (qval, numeric)

Linear Regression (method = 'lm')

For regression with 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

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

For classification using package e1071 with tuning parameters:

  • Cost (cost, numeric)
  • Class Weight (weight, numeric)

Localized Linear Discriminant Analysis (method = 'loclda')

For classification using package klaR with tuning parameters:

  • Number of Nearest Neighbors (k, numeric)

Logic Regression (method = 'logreg')

For classification and regression using package LogicReg with tuning parameters:

  • Maximum Number of Leaves (treesize, numeric)
  • Number of Trees (ntrees, numeric)

Logistic Model Trees (method = 'LMT')

For classification using package RWeka with tuning parameters:

  • Number of Iteratons (iter, numeric)

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

For classification using package HiDimDA with no tuning parameters

Mixture Discriminant Analysis (method = 'mda')

For classification using package mda with tuning parameters:

  • Number of Subclasses Per Class (subclasses, numeric)

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

For classification using package bnclassify with tuning parameters:

  • Smoothing Parameter (smooth, numeric)
  • Prior Probability (prior, numeric)

Model Averaged Neural Network (method = 'avNNet')

For classification and regression using package nnet with tuning parameters:

  • Number of Hidden Units (size, numeric)
  • Weight Decay (decay, numeric)
  • Bagging (bag, logical)

Model Rules (method = 'M5Rules')

For regression using package RWeka with tuning parameters:

  • Pruned (pruned, character)
  • Smoothed (smoothed, character)

Model Tree (method = 'M5')

For regression using package RWeka with tuning parameters:

  • Pruned (pruned, character)
  • Smoothed (smoothed, character)
  • Rules (rules, character)

Multi-Layer Perceptron (method = 'mlp')

For classification and regression using package RSNNS with tuning parameters:

  • Number of Hidden Units (size, numeric)

Multi-Layer Perceptron (method = 'mlpWeightDecay')

For classification and regression using package RSNNS with tuning parameters:

  • Number of Hidden Units (size, numeric)
  • Weight Decay (decay, numeric)

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

For classification and regression using package RSNNS with tuning parameters:

  • Number of Hidden Units layer1 (layer1, numeric)
  • Number of Hidden Units layer2 (layer2, numeric)
  • Number of Hidden Units layer3 (layer3, numeric)
  • Weight Decay (decay, numeric)

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

For classification and regression using package RSNNS with tuning parameters:

  • Number of Hidden Units layer1 (layer1, numeric)
  • Number of Hidden Units layer2 (layer2, numeric)
  • Number of Hidden Units layer3 (layer3, numeric)

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

For regression using package FCNN4R with tuning parameters:

  • Number of Hidden Units (size, numeric)
  • L2 Regularization (l2reg, numeric)
  • RMSE Gradient Scaling (lambda, numeric)
  • Learning Rate (learn_rate, numeric)
  • Momentum (momentum, numeric)
  • Decay (gamma, numeric)
  • Batch Size (minibatchsz, numeric)
  • Number of Models (repeats, numeric)

Multivariate Adaptive Regression Spline (method = 'earth')

For classification and regression using package earth with tuning parameters:

  • Number of Terms (nprune, numeric)
  • Product Degree (degree, numeric)

Multivariate Adaptive Regression Splines (method = 'gcvEarth')

For classification and regression using package earth with tuning parameters:

  • Product Degree (degree, numeric)

Naive Bayes (method = 'nb')

For classification using package klaR with tuning parameters:

  • Laplace Correction (fL, numeric)
  • Distribution Type (usekernel, logical)
  • Bandwidth Adjustment (adjust, numeric)

Naive Bayes Classifier (method = 'nbDiscrete')

For classification using package bnclassify with tuning parameters:

  • Smoothing Parameter (smooth, numeric)

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

For classification using package bnclassify with tuning parameters:

  • Smoothing Parameter (smooth, numeric)

Nearest Shrunken Centroids (method = 'pam')

For classification using package pamr with tuning parameters:

  • Shrinkage Threshold (threshold, numeric)

Neural Network (method = 'neuralnet')

For regression using package neuralnet with tuning parameters:

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

Neural Network (method = 'nnet')

For classification and regression using package nnet with tuning parameters:

  • Number of Hidden Units (size, numeric)
  • Weight Decay (decay, numeric)

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

For classification and regression using package nnet with tuning parameters:

  • Number of Hidden Units (size, numeric)
  • Weight Decay (decay, numeric)

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

For regression using package rqPen with tuning parameters:

  • L1 Penalty (lambda, numeric)
  • Penalty Type (penalty, character)

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

For regression using package nnls with no tuning parameters

Oblique Random Forest (method = 'ORFlog')

For classification using package obliqueRF with tuning parameters:

  • Number of Randomly Selected Predictors (mtry, numeric)

Oblique Random Forest (method = 'ORFpls')

For classification using package obliqueRF with tuning parameters:

  • Number of Randomly Selected Predictors (mtry, numeric)

Oblique Random Forest (method = 'ORFridge')

For classification using package obliqueRF with tuning parameters:

  • Number of Randomly Selected Predictors (mtry, numeric)

Oblique Random Forest (method = 'ORFsvm')

For classification using package obliqueRF with tuning parameters:

  • Number of Randomly Selected Predictors (mtry, numeric)

Oblique Trees (method = 'oblique.tree')

For classification using package oblique.tree with tuning parameters:

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

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

For classification using package snn with tuning parameters:

  • Number of Neighbors (K, numeric)

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

For classification using package MASS with tuning parameters:

  • parameter (method, character)

Parallel Random Forest (method = 'parRF')

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

  • Number of Randomly Selected Predictors (mtry, numeric)

partDSA (method = 'partDSA')

For classification and regression using package partDSA with tuning parameters:

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

Partial Least Squares (method = 'kernelpls')

For classification and regression using package pls with tuning parameters:

  • Number of Components (ncomp, numeric)

Partial Least Squares (method = 'pls')

For classification and regression using package pls with tuning parameters:

  • Number of Components (ncomp, numeric)

Partial Least Squares (method = 'simpls')

For classification and regression using package pls with tuning parameters:

  • Number of Components (ncomp, numeric)

Partial Least Squares (method = 'widekernelpls')

For classification and regression using package pls with tuning parameters:

  • Number of Components (ncomp, numeric)

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

For classification and regression using package plsRglm with tuning parameters:

  • Number of PLS Components (nt, numeric)
  • p-Value threshold (alpha.pvals.expli, numeric)

Penalized Discriminant Analysis (method = 'pda')

For classification using package mda with tuning parameters:

  • Shrinkage Penalty Coefficient (lambda, numeric)

Penalized Discriminant Analysis (method = 'pda2')

For classification using package mda with tuning parameters:

  • Degrees of Freedom (df, numeric)

Penalized Linear Discriminant Analysis (method = 'PenalizedLDA')

For classification using packages penalizedLDA and plyr with tuning parameters:

  • L1 Penalty (lambda, numeric)
  • Number of Discriminant Functions (K, numeric)

Penalized Linear Regression (method = 'penalized')

For regression using package penalized with tuning parameters:

  • L1 Penalty (lambda1, numeric)
  • L2 Penalty (lambda2, numeric)

Penalized Logistic Regression (method = 'plr')

For classification using package stepPlr with tuning parameters:

  • L2 Penalty (lambda, numeric)
  • Complexity Parameter (cp, character)

Penalized Multinomial Regression (method = 'multinom')

For classification using package nnet with tuning parameters:

  • Weight Decay (decay, numeric)

Penalized Ordinal Regression (method = 'ordinalNet')

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

  • Mixing Percentage (alpha, numeric)
  • Selection Criterion (criteria, character)
  • Link Function (link, character)

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

For regression using package KRLS with tuning parameters:

  • Regularization Parameter (lambda, numeric)
  • Polynomial Degree (degree, numeric)

Principal Component Analysis (method = 'pcr')

For regression using package pls with tuning parameters:

  • Number of Components (ncomp, numeric)

Projection Pursuit Regression (method = 'ppr')

For regression with tuning parameters:

  • Number of Terms (nterms, numeric)

Quadratic Discriminant Analysis (method = 'qda')

For classification using package MASS with no tuning parameters

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

For classification using packages klaR and MASS with tuning parameters:

  • Maximum Number of Variables (maxvar, numeric)
  • Search Direction (direction, character)

Quantile Random Forest (method = 'qrf')

For regression using package quantregForest with tuning parameters:

  • Number of Randomly Selected Predictors (mtry, numeric)

Quantile Regression Neural Network (method = 'qrnn')

For regression using package qrnn with tuning parameters:

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

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

For regression using package rqPen with tuning parameters:

  • L1 Penalty (lambda, numeric)

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

For regression using packages KRLS and kernlab with tuning parameters:

  • Regularization Parameter (lambda, numeric)
  • Sigma (sigma, numeric)

Radial Basis Function Network (method = 'rbf')

For classification and regression using package RSNNS with tuning parameters:

  • Number of Hidden Units (size, numeric)

Radial Basis Function Network (method = 'rbfDDA')

For classification and regression using package RSNNS with tuning parameters:

  • Activation Limit for Conflicting Classes (negativeThreshold, numeric)

Random Ferns (method = 'rFerns')

For classification using package rFerns with tuning parameters:

  • Fern Depth (depth, numeric)

Random Forest (method = 'ranger')

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

  • Number of Randomly Selected Predictors (mtry, numeric)

Random Forest (method = 'Rborist')

For classification and regression using package Rborist with tuning parameters:

  • Number of Randomly Selected Predictors (predFixed, numeric)

Random Forest (method = 'rf')

For classification and regression using package randomForest with tuning parameters:

  • Number of Randomly Selected Predictors (mtry, numeric)

Random Forest by Randomization (method = 'extraTrees')

For classification and regression using package extraTrees with tuning parameters:

  • Number of Randomly Selected Predictors (mtry, numeric)
  • Number of Random Cuts (numRandomCuts, numeric)

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

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

  • Number of Randomly Selected Predictors (mtry, numeric)
  • Maximum Rule Depth (maxdepth, numeric)

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

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

  • Number of Randomly Selected Predictors (mtry, numeric)

Regularized Discriminant Analysis (method = 'rda')

For classification using package klaR with tuning parameters:

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

Regularized Linear Discriminant Analysis (method = 'rlda')

For classification using package sparsediscrim with tuning parameters:

  • Regularization Method (estimator, character)

Regularized Random Forest (method = 'RRF')

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

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

Regularized Random Forest (method = 'RRFglobal')

For classification and regression using package RRF with tuning parameters:

  • Number of Randomly Selected Predictors (mtry, numeric)
  • Regularization Value (coefReg, numeric)

Relaxed Lasso (method = 'relaxo')

For regression using packages relaxo and plyr with tuning parameters:

  • Penalty Parameter (lambda, numeric)
  • Relaxation Parameter (phi, numeric)

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

For regression using package kernlab with no tuning parameters

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

For regression using package kernlab with tuning parameters:

  • Scale (scale, numeric)
  • Polynomial Degree (degree, numeric)

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

For regression using package kernlab with tuning parameters:

  • Sigma (sigma, numeric)

Ridge Regression (method = 'ridge')

For regression using package elasticnet with tuning parameters:

  • Weight Decay (lambda, numeric)

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

For regression using package foba with tuning parameters:

  • Number of Variables Retained (k, numeric)
  • L2 Penalty (lambda, numeric)

Robust Linear Discriminant Analysis (method = 'Linda')

For classification using package rrcov with no tuning parameters

Robust Linear Model (method = 'rlm')

For regression using package MASS with 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)

Rotation Forest (method = 'rotationForest')

For classification using package rotationForest with tuning parameters:

  • Number of Variable Subsets (K, numeric)
  • Ensemble Size (L, numeric)

Rotation Forest (method = 'rotationForestCp')

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

  • Number of Variable Subsets (K, numeric)
  • Ensemble Size (L, numeric)
  • Complexity Parameter (cp, numeric)

Rule-Based Classifier (method = 'JRip')

For classification using package RWeka with tuning parameters:

  • Number of Optimizations (NumOpt, numeric)

Rule-Based Classifier (method = 'PART')

For classification using package RWeka with tuning parameters:

  • Confidence Threshold (threshold, numeric)
  • Confidence Threshold (pruned, character)

Self-Organizing Map (method = 'bdk')

For classification and regression using package kohonen with tuning parameters:

  • Row (xdim, numeric)
  • Columns (ydim, numeric)
  • X Weight (xweight, numeric)
  • Topology (topo, character)

Self-Organizing Maps (method = 'xyf')

For classification and regression using package kohonen with tuning parameters:

  • Row (xdim, numeric)
  • Columns (ydim, numeric)
  • X Weight (xweight, numeric)
  • Topology (topo, character)

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

For classification using package bnclassify with tuning parameters:

  • Number of Folds (k, numeric)
  • Minimum Absolute Improvement (epsilon, numeric)
  • Smoothing Parameter (smooth, numeric)
  • Final Smoothing Parameter (final_smooth, numeric)
  • Search Direction (direction, character)

Shrinkage Discriminant Analysis (method = 'sda')

For classification using package sda with tuning parameters:

  • Diagonalize (diagonal, logical)
  • shrinkage (lambda, numeric)

SIMCA (method = 'CSimca')

For classification using package rrcovHD with no tuning parameters

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

For regression using package frbs with tuning parameters:

  • Number of Fuzzy Terms (num.labels, numeric)
  • Max. Iterations (max.iter, numeric)

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

For classification using package C50 with no tuning parameters

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

For classification using package C50 with no tuning parameters

Single Rule Classification (method = 'OneR')

For classification using package RWeka with no tuning parameters

Sparse Distance Weighted Discrimination (method = 'sdwd')

For classification using package sdwd with tuning parameters:

  • L1 Penalty (lambda, numeric)
  • L2 Penalty (lambda2, numeric)

Sparse Linear Discriminant Analysis (method = 'sparseLDA')

For classification using package sparseLDA with tuning parameters:

  • Number of Predictors (NumVars, numeric)
  • Lambda (lambda, numeric)

Sparse Mixture Discriminant Analysis (method = 'smda')

For classification using package sparseLDA with tuning parameters:

  • Number of Predictors (NumVars, numeric)
  • Lambda (lambda, numeric)
  • Number of Subclasses (R, numeric)

Sparse Partial Least Squares (method = 'spls')

For classification and regression using package spls with tuning parameters:

  • Number of Components (K, numeric)
  • Threshold (eta, numeric)
  • Kappa (kappa, numeric)

Spike and Slab Regression (method = 'spikeslab')

For regression using packages spikeslab and plyr with tuning parameters:

  • Variables Retained (vars, numeric)

Stabilized Linear Discriminant Analysis (method = 'slda')

For classification using package ipred with no tuning parameters

Stabilized Nearest Neighbor Classifier (method = 'snn')

For classification using package snn with tuning parameters:

  • Stabilization Parameter (lambda, numeric)

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

For classification and regression using package deepnet with tuning parameters:

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

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

For classification using package SDDA with no tuning parameters

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

For classification using package SDDA with no tuning parameters

Stochastic Gradient Boosting (method = 'gbm')

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

  • Number of Boosting Iterations (n.trees, numeric)
  • Max Tree Depth (interaction.depth, numeric)
  • Shrinkage (shrinkage, numeric)
  • Min. Terminal Node Size (n.minobsinnode, numeric)

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

For regression using package frbs with tuning parameters:

  • Radius (r.a, numeric)
  • Upper Threshold (eps.high, numeric)
  • Lower Threshold (eps.low, numeric)

Supervised Principal Component Analysis (method = 'superpc')

For regression using package superpc with tuning parameters:

  • Threshold (threshold, numeric)
  • Number of Components (n.components, numeric)

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

For classification and regression using package kernlab with tuning parameters:

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

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

For classification using package kernlab with tuning parameters:

  • Sigma (sigma, numeric)
  • Cost (C, numeric)
  • Weight (Weight, numeric)

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

For classification and regression using package kernlab with tuning parameters:

  • lambda (lambda, numeric)
  • Cost (C, numeric)

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

For classification and regression using package kernlab with tuning parameters:

  • Cost (C, numeric)

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

For classification and regression using package e1071 with tuning parameters:

  • Cost (cost, numeric)

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

For classification and regression using package kernlab with tuning parameters:

  • Polynomial Degree (degree, numeric)
  • Scale (scale, numeric)
  • Cost (C, numeric)

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

For classification and regression using package kernlab with tuning parameters:

  • Sigma (sigma, numeric)
  • Cost (C, numeric)

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

For classification and regression using package kernlab with tuning parameters:

  • Cost (C

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 6.0-70, License: GPL (>= 2)

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