R: A List of Available Models in train
train_model_list | R Documentation |
A List of Available Models in train
Description
These models are included in the package via wrappers for train . Custom models can also be created. See the URL below.
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:
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:
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:
Bagged Model (method = 'bag' )
For classification and regression using package caret with tuning parameters:
Bayesian Additive Regression Trees (method = 'bartMachine' )
For classification and regression using package bartMachine with tuning parameters:
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:
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:
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:
Boosted Logistic Regression (method = 'LogitBoost' )
For classification using package caTools with tuning parameters:
Boosted Smoothing Spline (method = 'bstSm' )
For classification and regression using packages bst and plyr with tuning parameters:
Boosted Tree (method = 'blackboost' )
For classification and regression using packages party, mboost and plyr with tuning parameters:
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:
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:
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:
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:
Conditional Inference Tree (method = 'ctree' )
For classification and regression using package party with tuning parameters:
Conditional Inference Tree (method = 'ctree2' )
For classification and regression using package party with tuning parameters:
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:
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:
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:
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:
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:
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:
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:
Gaussian Process with Radial Basis Function Kernel (method = 'gaussprRadial' )
For classification and regression using package kernlab with tuning parameters:
Generalized Additive Model using LOESS (method = 'gamLoess' )
For classification and regression using package gam with tuning parameters:
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:
Generalized Linear Model (method = 'glm' )
For classification and regression with no tuning parameters
Generalized Linear Model with Stepwise Feature Selection (method = 'glmStepAIC' )
For classification and regression using package MASS with no tuning parameters
Generalized Partial Least Squares (method = 'gpls' )
For classification using package gpls with tuning parameters:
Genetic Lateral Tuning and Rule Selection of Linguistic Fuzzy Systems (method = 'GFS.LT.RS' )
For regression using package frbs with tuning parameters:
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:
Heteroscedastic Discriminant Analysis (method = 'hda' )
For classification using package hda with tuning parameters:
High Dimensional Discriminant Analysis (method = 'hdda' )
For classification using package HDclassif with tuning parameters:
Threshold (threshold , character)
Model Type (model , numeric)
High-Dimensional Regularized Discriminant Analysis (method = 'hdrda' )
For classification using package sparsediscrim with tuning parameters:
Hybrid Neural Fuzzy Inference System (method = 'HYFIS' )
For regression using package frbs with tuning parameters:
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:
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:
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:
Least Angle Regression (method = 'lars2' )
For regression using package lars with tuning parameters:
Least Squares Support Vector Machine (method = 'lssvmLinear' )
For classification using package kernlab with no tuning parameters
Least Squares Support Vector Machine with Polynomial Kernel (method = 'lssvmPoly' )
For classification using package kernlab with tuning parameters:
Least Squares Support Vector Machine with Radial Basis Function Kernel (method = 'lssvmRadial' )
For classification using package kernlab with tuning parameters:
Linear Discriminant Analysis (method = 'lda' )
For classification using package MASS with no tuning parameters
Linear Discriminant Analysis (method = 'lda2' )
For classification using package MASS with tuning parameters:
Linear Discriminant Analysis with Stepwise Feature Selection (method = 'stepLDA' )
For classification using packages klaR and MASS with tuning parameters:
Maximum Number of Variables (maxvar , numeric)
Search Direction (direction , character)
Linear Distance Weighted Discrimination (method = 'dwdLinear' )
For classification using package kerndwd with tuning parameters:
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:
Linear Regression with Forward Selection (method = 'leapForward' )
For regression using package leaps with tuning parameters:
Linear Regression with Stepwise Selection (method = 'leapSeq' )
For regression using package leaps with tuning parameters:
Linear Regression with Stepwise Selection (method = 'lmStepAIC' )
For regression using package MASS with no tuning parameters
Linear Support Vector Machines with Class Weights (method = 'svmLinearWeights' )
For classification using package e1071 with tuning parameters:
Localized Linear Discriminant Analysis (method = 'loclda' )
For classification using package klaR with tuning parameters:
Logic Regression (method = 'logreg' )
For classification and regression using package LogicReg with tuning parameters:
Maximum Number of Leaves (treesize , numeric)
Number of Trees (ntrees , numeric)
Logistic Model Trees (method = 'LMT' )
For classification using package RWeka with tuning parameters:
Maximum Uncertainty Linear Discriminant Analysis (method = 'Mlda' )
For classification using package HiDimDA with no tuning parameters
Mixture Discriminant Analysis (method = 'mda' )
For classification using package mda with tuning parameters:
Model Averaged Naive Bayes Classifier (method = 'manb' )
For classification using package bnclassify with tuning parameters:
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:
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:
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:
Naive Bayes Classifier with Attribute Weighting (method = 'awnb' )
For classification using package bnclassify with tuning parameters:
Nearest Shrunken Centroids (method = 'pam' )
For classification using package pamr with tuning parameters:
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:
Oblique Random Forest (method = 'ORFpls' )
For classification using package obliqueRF with tuning parameters:
Oblique Random Forest (method = 'ORFridge' )
For classification using package obliqueRF with tuning parameters:
Oblique Random Forest (method = 'ORFsvm' )
For classification using package obliqueRF with tuning parameters:
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:
Ordered Logistic or Probit Regression (method = 'polr' )
For classification using package MASS with tuning parameters:
Parallel Random Forest (method = 'parRF' )
For classification and regression using packages e1071, randomForest and foreach with tuning parameters:
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:
Partial Least Squares (method = 'pls' )
For classification and regression using package pls with tuning parameters:
Partial Least Squares (method = 'simpls' )
For classification and regression using package pls with tuning parameters:
Partial Least Squares (method = 'widekernelpls' )
For classification and regression using package pls with tuning parameters:
Partial Least Squares Generalized Linear Models (method = 'plsRglm' )
For classification and regression using package plsRglm with tuning parameters:
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:
Penalized Discriminant Analysis (method = 'pda2' )
For classification using package mda with tuning parameters:
Penalized Linear Discriminant Analysis (method = 'PenalizedLDA' )
For classification using packages penalizedLDA and plyr with tuning parameters:
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:
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:
Projection Pursuit Regression (method = 'ppr' )
For regression with tuning parameters:
Quadratic Discriminant Analysis (method = 'qda' )
For classification using package MASS with no tuning parameters
Quadratic Discriminant Analysis with Stepwise Feature Selection (method = 'stepQDA' )
For classification using packages klaR and MASS with tuning parameters:
Maximum Number of Variables (maxvar , numeric)
Search Direction (direction , character)
Quantile Random Forest (method = 'qrf' )
For regression using package quantregForest with tuning parameters:
Quantile Regression Neural Network (method = 'qrnn' )
For regression using package qrnn with tuning parameters:
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:
Radial Basis Function Kernel Regularized Least Squares (method = 'krlsRadial' )
For regression using packages KRLS and kernlab with tuning parameters:
Radial Basis Function Network (method = 'rbf' )
For classification and regression using package RSNNS with tuning parameters:
Radial Basis Function Network (method = 'rbfDDA' )
For classification and regression using package RSNNS with tuning parameters:
Random Ferns (method = 'rFerns' )
For classification using package rFerns with tuning parameters:
Random Forest (method = 'ranger' )
For classification and regression using packages e1071 and ranger with tuning parameters:
Random Forest (method = 'Rborist' )
For classification and regression using package Rborist with tuning parameters:
Random Forest (method = 'rf' )
For classification and regression using package randomForest with tuning parameters:
Random Forest by Randomization (method = 'extraTrees' )
For classification and regression using package extraTrees with tuning parameters:
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:
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:
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:
Relevance Vector Machines with Radial Basis Function Kernel (method = 'rvmRadial' )
For regression using package kernlab with tuning parameters:
Ridge Regression (method = 'ridge' )
For regression using package elasticnet with tuning parameters:
Ridge Regression with Variable Selection (method = 'foba' )
For regression using package foba with tuning parameters:
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:
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:
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:
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:
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:
Spike and Slab Regression (method = 'spikeslab' )
For regression using packages spikeslab and plyr with tuning parameters:
Stabilized Linear Discriminant Analysis (method = 'slda' )
For classification using package ipred with no tuning parameters
Stabilized Nearest Neighbor Classifier (method = 'snn' )
For classification using package snn with tuning parameters:
Stacked AutoEncoder Deep Neural Network (method = 'dnn' )
For classification and regression using package deepnet with tuning parameters:
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:
Support Vector Machines with Linear Kernel (method = 'svmLinear2' )
For classification and regression using package e1071 with tuning parameters:
Support Vector Machines with Polynomial Kernel (method = 'svmPoly' )
For classification and regression using package kernlab with tuning parameters:
Support Vector Machines with Radial Basis Function Kernel (method = 'svmRadial' )
For classification and regression using package kernlab with tuning parameters:
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:
Support Vector Machines with Radial Basis Function Kernel (method = 'svmRadialSigma' )
For classification and regression using package kernlab with tuning parameters:
Sigma (sigma , numeric)
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 Bayesian lasso (method = 'blasso' )
For regression using package monomvn with tuning parameters:
The lasso (method = 'lasso' )
For regression using package elasticnet with tuning parameters:
Tree Augmented Naive Bayes Classifier (method = 'tan' )
For classification using package bnclassify with tuning parameters:
Score Function (score , character)
Smoothing Parameter (smooth , numeric)
Tree Augmented Naive Bayes Classifier Structure Learner Wrapper (method = 'tanSearch' )
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)
Super-Parent (sp , logical)
Tree Augmented Naive Bayes Classifier with Attribute Weighting (method = 'awtan' )
For classification using package bnclassify with tuning parameters:
Score Function (score , character)
Smoothing Parameter (smooth , numeric)
Tree Models from Genetic Algorithms (method = 'evtree' )
For classification and regression using package evtree with tuning parameters:
Tree-Based Ensembles (method = 'nodeHarvest' )
For classification and regression using package nodeHarvest with tuning parameters:
Maximum Interaction Depth (maxinter , numeric)
Prediction Mode (mode , character)
Variational Bayesian Multinomial Probit Regression (method = 'vbmpRadial' )
For classification using package vbmp with tuning parameters:
Wang and Mendel Fuzzy Rules (method = 'WM' )
For regression using package frbs with tuning parameters:
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:
References
“Using your own model in train ” (http://caret.r-forge.r-project.org/custom_models.html)
Results
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