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)
Minimum Instances Per Leaf (M, 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)
Subsample Percentage (subsample, numeric)
Extreme Learning Machine (method = 'elm')
For classification and regression using package elmNN with tuning parameters:
Number of Hidden Units (nhid, numeric)
Activation Function (actfun, character)
Factor-Based Linear Discriminant Analysis (method = 'RFlda')
For classification using package HiDimDA with tuning parameters:
Number of Factors (q, numeric)
Flexible Discriminant Analysis (method = 'fda')
For classification using packages earth and mda with tuning parameters:
Product Degree (degree, numeric)
Number of Terms (nprune, numeric)
Fuzzy Inference Rules by Descent Method (method = 'FIR.DM')
For regression using package frbs with tuning parameters:
Number of Fuzzy Terms (num.labels, numeric)
Max. Iterations (max.iter, numeric)
Fuzzy Rules Using Chi's Method (method = 'FRBCS.CHI')
For classification using package frbs with tuning parameters:
Number of Fuzzy Terms (num.labels, numeric)
Membership Function (type.mf, character)
Fuzzy Rules Using Genetic Cooperative-Competitive Learning (method = 'GFS.GCCL')
For classification using package frbs with tuning parameters:
Number of Fuzzy Terms (num.labels, numeric)
Population Size (popu.size, numeric)
Max. Generations (max.gen, numeric)
Fuzzy Rules Using Genetic Cooperative-Competitive Learning and Pittsburgh (method = 'FH.GBML')
For classification using package frbs with tuning parameters:
Max. Number of Rules (max.num.rule, numeric)
Population Size (popu.size, numeric)
Max. Generations (max.gen, numeric)
Fuzzy Rules Using the Structural Learning Algorithm on Vague Environment (method = 'SLAVE')
For classification using package frbs with tuning parameters:
Number of Fuzzy Terms (num.labels, numeric)
Max. Iterations (max.iter, numeric)
Max. Generations (max.gen, numeric)
Fuzzy Rules via MOGUL (method = 'GFS.FR.MOGUL')
For regression using package frbs with tuning parameters:
Max. Generations (max.gen, numeric)
Max. Iterations (max.iter, numeric)
Max. Tuning Iterations (max.tune, numeric)
Fuzzy Rules via Thrift (method = 'GFS.THRIFT')
For regression using package frbs with tuning parameters:
Population Size (popu.size, numeric)
Number of Fuzzy Labels (num.labels, numeric)
Max. Generations (max.gen, numeric)
Fuzzy Rules with Weight Factor (method = 'FRBCS.W')
For classification using package frbs with tuning parameters:
Number of Fuzzy Terms (num.labels, numeric)
Membership Function (type.mf, character)
Gaussian Process (method = 'gaussprLinear')
For classification and regression using package kernlab with no tuning parameters
Gaussian Process with Polynomial Kernel (method = 'gaussprPoly')
For classification and regression using package kernlab with tuning parameters:
Polynomial Degree (degree, numeric)
Scale (scale, numeric)
Gaussian Process with Radial Basis Function Kernel (method = 'gaussprRadial')
For classification and regression using package kernlab with tuning parameters:
Sigma (sigma, numeric)
Generalized Additive Model using LOESS (method = 'gamLoess')
For classification and regression using package gam with tuning parameters:
Span (span, numeric)
Degree (degree, numeric)
Generalized Additive Model using Splines (method = 'bam')
For classification and regression using package mgcv with tuning parameters:
Feature Selection (select, logical)
Method (method, character)
Generalized Additive Model using Splines (method = 'gam')
For classification and regression using package mgcv with tuning parameters:
Feature Selection (select, logical)
Method (method, character)
Generalized Additive Model using Splines (method = 'gamSpline')
For classification and regression using package gam with tuning parameters:
Degrees of Freedom (df, numeric)
Generalized Linear Model (method = 'glm')
For classification and regression with no tuning parameters
Generalized Linear Model with Stepwise Feature Selection (method = 'glmStepAIC')
For classification and regression using package MASS with no tuning parameters
Generalized Partial Least Squares (method = 'gpls')
For classification using package gpls with tuning parameters:
Number of Components (K.prov, numeric)
Genetic Lateral Tuning and Rule Selection of Linguistic Fuzzy Systems (method = 'GFS.LT.RS')
For regression using package frbs with tuning parameters:
Population Size (popu.size, numeric)
Number of Fuzzy Labels (num.labels, numeric)
Max. Generations (max.gen, numeric)
glmnet (method = 'gbm_h2o')
For classification and regression using package h2o with tuning parameters:
Number of Boosting Iterations (ntrees, numeric)
Max Tree Depth (max_depth, numeric)
Min. Terminal Node Size (min_rows, numeric)
Shrinkage (learn_rate, numeric)
Number of Randomly Selected Predictors (col_sample_rate, numeric)
glmnet (method = 'glmnet_h2o')
For classification and regression using package h2o with tuning parameters:
Mixing Percentage (alpha, numeric)
Regularization Parameter (lambda, numeric)
glmnet (method = 'glmnet')
For classification and regression using packages glmnet and Matrix with tuning parameters:
Mixing Percentage (alpha, numeric)
Regularization Parameter (lambda, numeric)
Greedy Prototype Selection (method = 'protoclass')
For classification using packages proxy and protoclass with tuning parameters:
Ball Size (eps, numeric)
Distance Order (Minkowski, numeric)
Heteroscedastic Discriminant Analysis (method = 'hda')
For classification using package hda with tuning parameters:
Gamma (gamma, numeric)
Lambda (lambda, numeric)
Dimension of the Discriminative Subspace (newdim, numeric)
High Dimensional Discriminant Analysis (method = 'hdda')
For classification using package HDclassif with tuning parameters:
Threshold (threshold, character)
Model Type (model, numeric)
High-Dimensional Regularized Discriminant Analysis (method = 'hdrda')
For classification using package sparsediscrim with tuning parameters:
Gamma (gamma, numeric)
Lambda (lambda, numeric)
Shrinkage Type (shrinkage_type, character)
Hybrid Neural Fuzzy Inference System (method = 'HYFIS')
For regression using package frbs with tuning parameters:
Number of Fuzzy Terms (num.labels, numeric)
Max. Iterations (max.iter, numeric)
Independent Component Regression (method = 'icr')
For regression using package fastICA with tuning parameters:
Number of Components (n.comp, numeric)
k-Nearest Neighbors (method = 'kknn')
For classification and regression using package kknn with tuning parameters:
Max. Number of Neighbors (kmax, numeric)
Distance (distance, numeric)
Kernel (kernel, character)
k-Nearest Neighbors (method = 'knn')
For classification and regression with tuning parameters:
Number of Neighbors (k, numeric)
Knn regression via sklearn.neighbors.KNeighborsRegressor (method = 'pythonKnnReg')
For regression using package rPython with tuning parameters:
Number of Neighbors (n_neighbors, numeric)
Weight Function (weights, character)
Algorithm (algorithm, character)
Leaf Size (leaf_size, numeric)
Distance Metric (metric, character)
p (p, numeric)
L2 Regularized Linear Support Vector Machines with Class Weights (method = 'svmLinearWeights2')
For classification using package LiblineaR with tuning parameters:
Cost (cost, numeric)
Loss Function (Loss, character)
Class Weight (weight, numeric)
L2 Regularized Support Vector Machine (dual) with Linear Kernel (method = 'svmLinear3')
For classification and regression using package LiblineaR with tuning parameters:
Cost (cost, numeric)
Loss Function (Loss, character)
Learning Vector Quantization (method = 'lvq')
For classification using package class with tuning parameters:
Codebook Size (size, numeric)
Number of Prototypes (k, numeric)
Least Angle Regression (method = 'lars')
For regression using package lars with tuning parameters:
Fraction (fraction, numeric)
Least Angle Regression (method = 'lars2')
For regression using package lars with tuning parameters:
Number of Steps (step, numeric)
Least Squares Support Vector Machine (method = 'lssvmLinear')
For classification using package kernlab with tuning parameters:
Regularization Parameter (tau, numeric)
Least Squares Support Vector Machine with Polynomial Kernel (method = 'lssvmPoly')
For classification using package kernlab with tuning parameters:
Polynomial Degree (degree, numeric)
Scale (scale, numeric)
Regularization Parameter (tau, numeric)
Least Squares Support Vector Machine with Radial Basis Function Kernel (method = 'lssvmRadial')
For classification using package kernlab with tuning parameters:
Sigma (sigma, numeric)
Regularization Parameter (tau, numeric)
Linear Discriminant Analysis (method = 'lda')
For classification using package MASS with no tuning parameters
Linear Discriminant Analysis (method = 'lda2')
For classification using package MASS with tuning parameters:
Number of Discriminant Functions (dimen, numeric)
Linear Discriminant Analysis with Stepwise Feature Selection (method = 'stepLDA')
For classification using packages klaR and MASS with tuning parameters:
Maximum Number of Variables (maxvar, numeric)
Search Direction (direction, character)
Linear Distance Weighted Discrimination (method = 'dwdLinear')
For classification using package kerndwd with tuning parameters:
Regularization Parameter (lambda, numeric)
q (qval, numeric)
Linear Regression (method = 'lm')
For regression with tuning parameters:
intercept (intercept, logical)
Linear Regression with Backwards Selection (method = 'leapBackward')
For regression using package leaps with tuning parameters:
Maximum Number of Predictors (nvmax, numeric)
Linear Regression with Forward Selection (method = 'leapForward')
For regression using package leaps with tuning parameters:
Maximum Number of Predictors (nvmax, numeric)
Linear Regression with Stepwise Selection (method = 'leapSeq')
For regression using package leaps with tuning parameters:
Maximum Number of Predictors (nvmax, numeric)
Linear Regression with Stepwise Selection (method = 'lmStepAIC')
For regression using package MASS with no tuning parameters
Linear Support Vector Machines with Class Weights (method = 'svmLinearWeights')
For classification using package e1071 with tuning parameters:
Cost (cost, numeric)
Class Weight (weight, numeric)
Localized Linear Discriminant Analysis (method = 'loclda')
For classification using package klaR with tuning parameters:
Number of Nearest Neighbors (k, numeric)
Logic Regression (method = 'logreg')
For classification and regression using package LogicReg with tuning parameters:
Maximum Number of Leaves (treesize, numeric)
Number of Trees (ntrees, numeric)
Logistic Model Trees (method = 'LMT')
For classification using package RWeka with tuning parameters:
Number of Iteratons (iter, numeric)
Maximum Uncertainty Linear Discriminant Analysis (method = 'Mlda')
For classification using package HiDimDA with no tuning parameters
Mixture Discriminant Analysis (method = 'mda')
For classification using package mda with tuning parameters:
Number of Subclasses Per Class (subclasses, numeric)
Model Averaged Naive Bayes Classifier (method = 'manb')
For classification using package bnclassify with tuning parameters:
Smoothing Parameter (smooth, numeric)
Prior Probability (prior, numeric)
Model Averaged Neural Network (method = 'avNNet')
For classification and regression using package nnet with tuning parameters:
Number of Hidden Units (size, numeric)
Weight Decay (decay, numeric)
Bagging (bag, logical)
Model Rules (method = 'M5Rules')
For regression using package RWeka with tuning parameters:
Pruned (pruned, character)
Smoothed (smoothed, character)
Model Tree (method = 'M5')
For regression using package RWeka with tuning parameters:
Pruned (pruned, character)
Smoothed (smoothed, character)
Rules (rules, character)
Multi-Layer Perceptron (method = 'mlp')
For classification and regression using package RSNNS with tuning parameters:
Number of Hidden Units (size, numeric)
Multi-Layer Perceptron (method = 'mlpWeightDecay')
For classification and regression using package RSNNS with tuning parameters:
Number of Hidden Units (size, numeric)
Weight Decay (decay, numeric)
Multi-Layer Perceptron, multiple layers (method = 'mlpWeightDecayML')
For classification and regression using package RSNNS with tuning parameters:
Number of Hidden Units layer1 (layer1, numeric)
Number of Hidden Units layer2 (layer2, numeric)
Number of Hidden Units layer3 (layer3, numeric)
Weight Decay (decay, numeric)
Multi-Layer Perceptron, with multiple layers (method = 'mlpML')
For classification and regression using package RSNNS with tuning parameters:
Number of Hidden Units layer1 (layer1, numeric)
Number of Hidden Units layer2 (layer2, numeric)
Number of Hidden Units layer3 (layer3, numeric)
Multilayer Perceptron Network by Stochastic Gradient Descent (method = 'mlpSGD')
For classification and regression using packages FCNN4R and plyr 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)
Negative Binomial Generalized Linear Model (method = 'glm.nb')
For regression with tuning parameters:
Link Function (link, character)
Neural Network (method = 'neuralnet')
For regression using package neuralnet with tuning parameters:
Number of Hidden Units in Layer 1 (layer1, numeric)
Number of Hidden Units in Layer 2 (layer2, numeric)
Number of Hidden Units in Layer 3 (layer3, numeric)
Neural Network (method = 'nnet')
For classification and regression using package nnet with tuning parameters:
Number of Hidden Units (size, numeric)
Weight Decay (decay, numeric)
Neural Networks with Feature Extraction (method = 'pcaNNet')
For classification and regression using package nnet with tuning parameters:
Number of Hidden Units (size, numeric)
Weight Decay (decay, numeric)
Non-Convex Penalized Quantile Regression (method = 'rqnc')
For regression using package rqPen with tuning parameters:
L1 Penalty (lambda, numeric)
Penalty Type (penalty, character)
Non-Negative Least Squares (method = 'nnls')
For regression using package nnls with no tuning parameters
Oblique Random Forest (method = 'ORFlog')
For classification using package obliqueRF with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
Oblique Random Forest (method = 'ORFpls')
For classification using package obliqueRF with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
Oblique Random Forest (method = 'ORFridge')
For classification using package obliqueRF with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
Oblique Random Forest (method = 'ORFsvm')
For classification using package obliqueRF with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
Oblique Trees (method = 'oblique.tree')
For classification using package oblique.tree with tuning parameters:
Oblique Splits (oblique.splits, character)
Variable Selection Method (variable.selection, character)
Optimal Weighted Nearest Neighbor Classifier (method = 'ownn')
For classification using package snn with tuning parameters:
Number of Neighbors (K, numeric)
Ordered Logistic or Probit Regression (method = 'polr')
For classification using package MASS with tuning parameters:
parameter (method, character)
Parallel Random Forest (method = 'parRF')
For classification and regression using packages e1071, randomForest and foreach with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
partDSA (method = 'partDSA')
For classification and regression using package partDSA with tuning parameters:
Number of Terminal Partitions (cut.off.growth, numeric)
Minimum Percent Difference (MPD, numeric)
Partial Least Squares (method = 'kernelpls')
For classification and regression using package pls with tuning parameters:
Number of Components (ncomp, numeric)
Partial Least Squares (method = 'pls')
For classification and regression using package pls with tuning parameters:
Number of Components (ncomp, numeric)
Partial Least Squares (method = 'simpls')
For classification and regression using package pls with tuning parameters:
Number of Components (ncomp, numeric)
Partial Least Squares (method = 'widekernelpls')
For classification and regression using package pls with tuning parameters:
Number of Components (ncomp, numeric)
Partial Least Squares Generalized Linear Models (method = 'plsRglm')
For classification and regression using package plsRglm with tuning parameters:
Number of PLS Components (nt, numeric)
p-Value threshold (alpha.pvals.expli, numeric)
Penalized Discriminant Analysis (method = 'pda')
For classification using package mda with tuning parameters:
Shrinkage Penalty Coefficient (lambda, numeric)
Penalized Discriminant Analysis (method = 'pda2')
For classification using package mda with tuning parameters:
Degrees of Freedom (df, numeric)
Penalized Linear Discriminant Analysis (method = 'PenalizedLDA')
For classification using packages penalizedLDA and plyr with tuning parameters:
L1 Penalty (lambda, numeric)
Number of Discriminant Functions (K, numeric)
Penalized Linear Regression (method = 'penalized')
For regression using package penalized with tuning parameters:
L1 Penalty (lambda1, numeric)
L2 Penalty (lambda2, numeric)
Penalized Logistic Regression (method = 'plr')
For classification using package stepPlr with tuning parameters:
L2 Penalty (lambda, numeric)
Complexity Parameter (cp, character)
Penalized Multinomial Regression (method = 'multinom')
For classification using package nnet with tuning parameters:
Weight Decay (decay, numeric)
Penalized Ordinal Regression (method = 'ordinalNet')
For classification and regression using packages ordinalNet and plyr with tuning parameters:
Mixing Percentage (alpha, numeric)
Selection Criterion (criteria, character)
Link Function (link, character)
Polynomial Kernel Regularized Least Squares (method = 'krlsPoly')
For regression using package KRLS with tuning parameters:
Regularization Parameter (lambda, numeric)
Polynomial Degree (degree, numeric)
Principal Component Analysis (method = 'pcr')
For regression using package pls with tuning parameters:
Number of Components (ncomp, numeric)
Projection Pursuit Regression (method = 'ppr')
For regression with tuning parameters:
Number of Terms (nterms, numeric)
Quadratic Discriminant Analysis (method = 'qda')
For classification using package MASS with no tuning parameters
Quadratic Discriminant Analysis with Stepwise Feature Selection (method = 'stepQDA')
For classification using packages klaR and MASS with tuning parameters:
Maximum Number of Variables (maxvar, numeric)
Search Direction (direction, character)
Quantile Random Forest (method = 'qrf')
For regression using package quantregForest with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
Quantile Regression Neural Network (method = 'qrnn')
For regression using package qrnn with tuning parameters:
Number of Hidden Units (n.hidden, numeric)
Weight Decay (penalty, numeric)
Bagged Models? (bag, logical)
Quantile Regression with LASSO penalty (method = 'rqlasso')
For regression using package rqPen with tuning parameters:
L1 Penalty (lambda, numeric)
Radial Basis Function Kernel Regularized Least Squares (method = 'krlsRadial')
For regression using packages KRLS and kernlab with tuning parameters:
Regularization Parameter (lambda, numeric)
Sigma (sigma, numeric)
Radial Basis Function Network (method = 'rbf')
For classification and regression using package RSNNS with tuning parameters:
Number of Hidden Units (size, numeric)
Radial Basis Function Network (method = 'rbfDDA')
For classification and regression using package RSNNS with tuning parameters:
Activation Limit for Conflicting Classes (negativeThreshold, numeric)
Random Ferns (method = 'rFerns')
For classification using package rFerns with tuning parameters:
Fern Depth (depth, numeric)
Random Forest (method = 'ranger')
For classification and regression using packages e1071 and ranger with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
Random Forest (method = 'Rborist')
For classification and regression using package Rborist with tuning parameters:
Number of Randomly Selected Predictors (predFixed, numeric)
Random Forest (method = 'rf')
For classification and regression using package randomForest with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
Random Forest by Randomization (method = 'extraTrees')
For classification and regression using package extraTrees with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
Number of Random Cuts (numRandomCuts, numeric)
Random Forest Rule-Based Model (method = 'rfRules')
For classification and regression using packages randomForest, inTrees and plyr with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
Maximum Rule Depth (maxdepth, numeric)
Random Forest with Additional Feature Selection (method = 'Boruta')
For classification and regression using packages Boruta and randomForest with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
Regularized Discriminant Analysis (method = 'rda')
For classification using package klaR with tuning parameters:
Gamma (gamma, numeric)
Lambda (lambda, numeric)
Regularized Linear Discriminant Analysis (method = 'rlda')
For classification using package sparsediscrim with tuning parameters:
Regularization Method (estimator, character)
Regularized Random Forest (method = 'RRF')
For classification and regression using packages randomForest and RRF with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
Regularization Value (coefReg, numeric)
Importance Coefficient (coefImp, numeric)
Regularized Random Forest (method = 'RRFglobal')
For classification and regression using package RRF with tuning parameters:
Number of Randomly Selected Predictors (mtry, numeric)
Regularization Value (coefReg, numeric)
Relaxed Lasso (method = 'relaxo')
For regression using packages relaxo and plyr with tuning parameters:
Penalty Parameter (lambda, numeric)
Relaxation Parameter (phi, numeric)
Relevance Vector Machines with Linear Kernel (method = 'rvmLinear')
For regression using package kernlab with no tuning parameters
Relevance Vector Machines with Polynomial Kernel (method = 'rvmPoly')
For regression using package kernlab with tuning parameters:
Scale (scale, numeric)
Polynomial Degree (degree, numeric)
Relevance Vector Machines with Radial Basis Function Kernel (method = 'rvmRadial')
For regression using package kernlab with tuning parameters:
Sigma (sigma, numeric)
Ridge Regression (method = 'ridge')
For regression using package elasticnet with tuning parameters:
Weight Decay (lambda, numeric)
Ridge Regression with Variable Selection (method = 'foba')
For regression using package foba with tuning parameters:
Number of Variables Retained (k, numeric)
L2 Penalty (lambda, numeric)
Robust Linear Discriminant Analysis (method = 'Linda')
For classification using package rrcov with no tuning parameters
Robust Linear Model (method = 'rlm')
For regression using package MASS with tuning parameters:
intercept (intercept, logical)
psi (psi, character)
Robust Mixture Discriminant Analysis (method = 'rmda')
For classification using package robustDA with tuning parameters:
Number of Subclasses Per Class (K, numeric)
Model (model, character)
Robust Quadratic Discriminant Analysis (method = 'QdaCov')
For classification using package rrcov with no tuning parameters
Robust Regularized Linear Discriminant Analysis (method = 'rrlda')
For classification using package rrlda with tuning parameters:
Penalty Parameter (lambda, numeric)
Robustness Parameter (hp, numeric)
Penalty Type (penalty, character)
Robust SIMCA (method = 'RSimca')
For classification using package rrcovHD with no tuning parameters
ROC-Based Classifier (method = 'rocc')
For classification using package rocc with tuning parameters:
Number of Variables Retained (xgenes, numeric)
Rotation Forest (method = 'rotationForest')
For classification using package rotationForest with tuning parameters:
Number of Variable Subsets (K, numeric)
Ensemble Size (L, numeric)
Rotation Forest (method = 'rotationForestCp')
For classification using packages rpart, plyr and rotationForest with tuning parameters:
Number of Variable Subsets (K, numeric)
Ensemble Size (L, numeric)
Complexity Parameter (cp, numeric)
Rule-Based Classifier (method = 'JRip')
For classification using package RWeka with tuning parameters:
Number of Optimizations (NumOpt, numeric)
Number of Folds (NumFolds, numeric)
Min Weights (MinWeights, numeric)
Rule-Based Classifier (method = 'PART')
For classification using package RWeka with tuning parameters:
Confidence Threshold (threshold, numeric)
Pruning (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
``Using your own model in train'' (https://topepo.github.io/caret/using-your-own-model-in-train.html)