200+ Machine Learning Algorithms of caret Package in R

When we build a model we have to use a machine learning algorithm to train our model. There are 200+ machine learning algorithms in the caret package.  When we build models we do not use only an algorithm. We use different algorithms to build multiple models and select the best one among them which gives the best result. Caret Package is a comprehensive framework for building machine learning models in R. To know the available algorithms of caret package in R, we can write following command:

modelnames <- paste(names(getModelInfo()), collapse=', ')

'ada, AdaBag, AdaBoost.M1, adaboost, amdai, ANFIS, avNNet, awnb, awtan, bag, bagEarth, bagEarthGCV, bagFDA, bagFDAGCV, bam, bartMachine, bayesglm, binda, blackboost, blasso, blassoAveraged, bridge, brnn, BstLm, bstSm, bstTree, C5.0, C5.0Cost, C5.0Rules, C5.0Tree, cforest, chaid, CSimca, ctree, ctree2, cubist, dda, deepboost, DENFIS, dnn, dwdLinear, dwdPoly, dwdRadial, earth, elm, enet, evtree, extraTrees, fda, FH.GBML, FIR.DM, foba, FRBCS.CHI, FRBCS.W, FS.HGD, gam, gamboost, gamLoess, gamSpline, gaussprLinear, gaussprPoly, gaussprRadial, gbmh2o, gbm, gcvEarth, GFS.FR.MOGUL, GFS.LT.RS, GFS.THRIFT, glm.nb, glm, glmboost, glmneth2o, glmnet, glmStepAIC, gpls, hda, hdda, hdrda, HYFIS, icr, J48, JRip, kernelpls, kknn, knn, krlsPoly, krlsRadial, lars, lars2, lasso, lda, lda2, leapBackward, leapForward, leapSeq, Linda, lm, lmStepAIC, LMT, loclda, logicBag, LogitBoost, logreg, lssvmLinear, lssvmPoly, lssvmRadial, lvq, M5, M5Rules, manb, mda, Mlda, mlp, mlpKerasDecay, mlpKerasDecayCost, mlpKerasDropout, mlpKerasDropoutCost, mlpML, mlpSGD, mlpWeightDecay, mlpWeightDecayML, monmlp, msaenet, multinom, mxnet, mxnetAdam, naive_bayes, nb, nbDiscrete, nbSearch, neuralnet, nnet, nnls, nodeHarvest, null, OneR, ordinalNet, ORFlog, ORFpls, ORFridge, ORFsvm, ownn, pam, parRF, PART, partDSA, pcaNNet, pcr, pda, pda2, penalized, PenalizedLDA, plr, pls, plsRglm, polr, ppr, PRIM, protoclass, pythonKnnReg, qda, QdaCov, qrf, qrnn, randomGLM, ranger, rbf, rbfDDA, Rborist, rda, regLogistic, relaxo, rf, rFerns, RFlda, rfRules, ridge, rlda, rlm, rmda, rocc, rotationForest, rotationForestCp, rpart, rpart1SE, rpart2, rpartCost, rpartScore, rqlasso, rqnc, RRF, RRFglobal, rrlda, RSimca, rvmLinear, rvmPoly, rvmRadial, SBC, sda, sdwd, simpls, SLAVE, slda, smda, snn, sparseLDA, spikeslab, spls, stepLDA, stepQDA, superpc, svmBoundrangeString, svmExpoString, svmLinear, svmLinear2, svmLinear3, svmLinearWeights, svmLinearWeights2, svmPoly, svmRadial, svmRadialCost, svmRadialSigma, svmRadialWeights, svmSpectrumString, tan, tanSearch, treebag, vbmpRadial, vglmAdjCat, vglmContRatio, vglmCumulative, widekernelpls, WM, wsrf, xgbDART, xgbLinear, xgbTree, xyf'

Each of those is a machine learning algorithm caret supports. Yes, it’s a huge list! And if you want to know more details like the hyperparameters and if it can be used of regression or classification problem, then write the following command in R:


Here are the brief information about the above algorithms with their parameters:

Sl Algorithm Name method Value AlgorithmType Libraries Tuning Parameters
1 AdaBoost Classification Trees adaboost Classification fastAdaboost nIter, method
2 AdaBoost.M1 AdaBoost.M1 Classification adabag, plyr mfinal, maxdepth, coeflearn
3 Adaptive Mixture Discriminant Analysis amdai Classification adaptDA model
4 Adaptive-Network-Based Fuzzy Inference System ANFIS Regression frbs num.labels, max.iter
5 Adjacent Categories Probability Model for Ordinal Data vglmAdjCat Classification VGAM parallel, link
6 Bagged AdaBoost AdaBag Classification adabag, plyr mfinal, maxdepth
7 Bagged CART treebag Classification, Regression ipred, plyr, e1071 None
8 Bagged FDA using gCV Pruning bagFDAGCV Classification earth degree
9 Bagged Flexible Discriminant Analysis bagFDA Classification earth, mda degree, nprune
10 Bagged Logic Regression logicBag Classification, Regression logicFS nleaves, ntrees
11 Bagged MARS bagEarth Classification, Regression earth nprune, degree
12 Bagged MARS using gCV Pruning bagEarthGCV Classification, Regression earth degree
13 Bagged Model bag Classification, Regression caret vars
14 Bayesian Additive Regression Trees bartMachine Classification, Regression bartMachine num_trees, k, alpha, beta, nu
15 Bayesian Generalized Linear Model bayesglm Classification, Regression arm None
16 Bayesian Regularized Neural Networks brnn Regression brnn neurons
17 Bayesian Ridge Regression bridge Regression monomvn None
18 Bayesian Ridge Regression (Model Averaged) blassoAveraged Regression monomvn None
19 Binary Discriminant Analysis binda Classification binda lambda.freqs
20 Boosted Classification Trees ada Classification ada, plyr iter, maxdepth, nu
21 Boosted Generalized Additive Model gamboost Classification, Regression mboost, plyr, import mstop, prune
22 Boosted Generalized Linear Model glmboost Classification, Regression plyr, mboost mstop, prune
23 Boosted Linear Model BstLm Classification, Regression bst, plyr mstop, nu
24 Boosted Logistic Regression LogitBoost Classification caTools nIter
25 Boosted Smoothing Spline bstSm Classification, Regression bst, plyr mstop, nu
26 Boosted Tree blackboost Classification, Regression party, mboost, plyr, partykit mstop, maxdepth
27 Boosted Tree bstTree Classification, Regression bst, plyr mstop, maxdepth, nu
28 C4.5-like Trees J48 Classification RWeka C, M
29 C5.0 C5.0 Classification C50, plyr trials, model, winnow
30 CART rpart Classification, Regression rpart cp
31 CART rpart1SE Classification, Regression rpart None
32 CART rpart2 Classification, Regression rpart maxdepth
33 CART or Ordinal Responses rpartScore Classification rpartScore, plyr cp, split, prune
34 CHi-squared Automated Interaction Detection chaid Classification CHAID alpha2, alpha3, alpha4
35 Conditional Inference Random Forest cforest Classification, Regression party mtry
36 Conditional Inference Tree ctree Classification, Regression party mincriterion
37 Conditional Inference Tree ctree2 Classification, Regression party maxdepth, mincriterion
38 Continuation Ratio Model for Ordinal Data vglmContRatio Classification VGAM parallel, link
39 Cost-Sensitive C5.0 C5.0Cost Classification C50, plyr trials, model, winnow, cost
40 Cost-Sensitive CART rpartCost Classification rpart, plyr cp, Cost
41 Cubist cubist Regression Cubist committees, neighbors
42 Cumulative Probability Model for Ordinal Data vglmCumulative Classification VGAM parallel, link
43 DeepBoost deepboost Classification deepboost num_iter, tree_depth, beta, lambda, loss_type
44 Diagonal Discriminant Analysis dda Classification sparsediscrim model, shrinkage
45 Distance Weighted Discrimination with Polynomial Kernel dwdPoly Classification kerndwd lambda, qval, degree, scale
46 Distance Weighted Discrimination with Radial Basis Function Kernel dwdRadial Classification kernlab, kerndwd lambda, qval, sigma
47 Dynamic Evolving Neural-Fuzzy Inference System DENFIS Regression frbs Dthr, max.iter
48 Elasticnet enet Regression elasticnet fraction, lambda
49 Ensembles of Generalized Linear Models randomGLM Classification, Regression randomGLM maxInteractionOrder
50 eXtreme Gradient Boosting xgbDART Classification, Regression xgboost, plyr nrounds, max_depth, eta, gamma, subsample, colsample_bytree, rate_drop, skip_drop, min_child_weight
51 eXtreme Gradient Boosting xgbLinear Classification, Regression xgboost nrounds, lambda, alpha, eta
52 eXtreme Gradient Boosting xgbTree Classification, Regression xgboost, plyr nrounds, max_depth, eta, gamma, colsample_bytree, min_child_weight, subsample
53 Extreme Learning Machine elm Classification, Regression elmNN nhid, actfun
54 Factor-Based Linear Discriminant Analysis RFlda Classification HiDimDA q
55 Flexible Discriminant Analysis fda Classification earth, mda degree, nprune
56 Fuzzy Inference Rules by Descent Method FIR.DM Regression frbs num.labels, max.iter
57 Fuzzy Rules Using Chi’s Method FRBCS.CHI Classification frbs num.labels, type.mf
58 Fuzzy Rules Using Genetic Cooperative-Competitive Learning and Pittsburgh FH.GBML Classification frbs max.num.rule, popu.size, max.gen
59 Fuzzy Rules Using the Structural Learning Algorithm on Vague Environment SLAVE Classification frbs num.labels, max.iter, max.gen
60 Fuzzy Rules via MOGUL GFS.FR.MOGUL Regression frbs max.gen, max.iter, max.tune
61 Fuzzy Rules via Thrift GFS.THRIFT Regression frbs popu.size, num.labels, max.gen
62 Fuzzy Rules with Weight Factor FRBCS.W Classification frbs num.labels, type.mf
63 Gaussian Process gaussprLinear Classification, Regression kernlab None
64 Gaussian Process with Polynomial Kernel gaussprPoly Classification, Regression kernlab degree, scale
65 Gaussian Process with Radial Basis Function Kernel gaussprRadial Classification, Regression kernlab sigma
66 Generalized Additive Model using LOESS gamLoess Classification, Regression gam span, degree
67 Generalized Additive Model using Splines bam Classification, Regression mgcv select, method
68 Generalized Additive Model using Splines gam Classification, Regression mgcv select, method
69 Generalized Additive Model using Splines gamSpline Classification, Regression gam df
70 Generalized Linear Model glm Classification, Regression None
71 Generalized Linear Model with Stepwise Feature Selection glmStepAIC Classification, Regression MASS None
72 Generalized Partial Least Squares gpls Classification gpls K.prov
73 Genetic Lateral Tuning and Rule Selection of Linguistic Fuzzy Systems GFS.LT.RS Regression frbs popu.size, num.labels, max.gen
74 glmnet glmnet Classification, Regression glmnet, Matrix alpha, lambda
75 glmnet glmnet_h2o Classification, Regression h2o alpha, lambda
76 Gradient Boosting Machines gbm_h2o Classification, Regression h2o ntrees, max_depth, min_rows, learn_rate, col_sample_rate
77 Greedy Prototype Selection protoclass Classification proxy, protoclass eps, Minkowski
78 Heteroscedastic Discriminant Analysis hda Classification hda gamma, lambda, newdim
79 High Dimensional Discriminant Analysis hdda Classification HDclassif threshold, model
80 High-Dimensional Regularized Discriminant Analysis hdrda Classification sparsediscrim gamma, lambda, shrinkage_type
81 Hybrid Neural Fuzzy Inference System HYFIS Regression frbs num.labels, max.iter
82 Independent Component Regression icr Regression fastICA n.comp
83 k-Nearest Neighbors kknn Classification, Regression kknn kmax, distance, kernel
84 k-Nearest Neighbors knn Classification, Regression k
85 L2 Regularized Linear Support Vector Machines with Class Weights svmLinearWeights2 Classification LiblineaR cost, Loss, weight
86 L2 Regularized Support Vector Machine (dual) with Linear Kernel svmLinear3 Classification, Regression LiblineaR cost, Loss
87 Learning Vector Quantization lvq Classification class size, k
88 Least Angle Regression lars Regression lars fraction
89 Least Angle Regression lars2 Regression lars step
90 Least Squares Support Vector Machine lssvmLinear Classification kernlab tau
91 Least Squares Support Vector Machine with Polynomial Kernel lssvmPoly Classification kernlab degree, scale, tau
92 Least Squares Support Vector Machine with Radial Basis Function Kernel lssvmRadial Classification kernlab sigma, tau
93 Linear Discriminant Analysis lda Classification MASS None
94 Linear Discriminant Analysis lda2 Classification MASS dimen
95 Linear Discriminant Analysis with Stepwise Feature Selection stepLDA Classification klaR, MASS maxvar, direction
96 Linear Distance Weighted Discrimination dwdLinear Classification kerndwd lambda, qval
97 Linear Regression lm Regression intercept
98 Linear Regression with Backwards Selection leapBackward Regression leaps nvmax
99 Linear Regression with Forward Selection leapForward Regression leaps nvmax
100 Linear Regression with Stepwise Selection leapSeq Regression leaps nvmax
101 Linear Regression with Stepwise Selection lmStepAIC Regression MASS None
102 Linear Support Vector Machines with Class Weights svmLinearWeights Classification e1071 cost, weight
103 Localized Linear Discriminant Analysis loclda Classification klaR k
104 Logic Regression logreg Classification, Regression LogicReg treesize, ntrees
105 Logistic Model Trees LMT Classification RWeka iter
106 Maximum Uncertainty Linear Discriminant Analysis Mlda Classification HiDimDA None
107 Mixture Discriminant Analysis mda Classification mda subclasses
108 Model Averaged Naive Bayes Classifier manb Classification bnclassify smooth, prior
109 Model Averaged Neural Network avNNet Classification, Regression nnet size, decay, bag
110 Model Rules M5Rules Regression RWeka pruned, smoothed
111 Model Tree M5 Regression RWeka pruned, smoothed, rules
112 Monotone Multi-Layer Perceptron Neural Network monmlp Classification, Regression monmlp hidden1, n.ensemble
113 Multi-Layer Perceptron mlp Classification, Regression RSNNS size
114 Multi-Layer Perceptron mlpWeightDecay Classification, Regression RSNNS size, decay
115 Multi-Layer Perceptron, multiple layers mlpWeightDecayML Classification, Regression RSNNS layer1, layer2, layer3, decay
116 Multi-Layer Perceptron, with multiple layers mlpML Classification, Regression RSNNS layer1, layer2, layer3
117 Multi-Step Adaptive MCP-Net msaenet Classification, Regression msaenet alphas, nsteps, scale
118 Multilayer Perceptron Network by Stochastic Gradient Descent mlpSGD Classification, Regression FCNN4R, plyr size, l2reg, lambda, learn_rate, momentum, gamma, minibatchsz, repeats
119 Multilayer Perceptron Network with Dropout mlpKerasDropout Classification, Regression keras size, dropout, batch_size, lr, rho, decay, activation
120 Multilayer Perceptron Network with Dropout mlpKerasDropoutCost Classification keras size, dropout, batch_size, lr, rho, decay, cost, activation
121 Multilayer Perceptron Network with Weight Decay mlpKerasDecay Classification, Regression keras size, lambda, batch_size, lr, rho, decay, activation
122 Multilayer Perceptron Network with Weight Decay mlpKerasDecayCost Classification keras size, lambda, batch_size, lr, rho, decay, cost, activation
123 Multivariate Adaptive Regression Spline earth Classification, Regression earth nprune, degree
124 Multivariate Adaptive Regression Splines gcvEarth Classification, Regression earth degree
125 Naive Bayes naive_bayes Classification naivebayes laplace, usekernel, adjust
126 Naive Bayes nb Classification klaR fL, usekernel, adjust
127 Naive Bayes Classifier nbDiscrete Classification bnclassify smooth
128 Naive Bayes Classifier with Attribute Weighting awnb Classification bnclassify smooth
129 Nearest Shrunken Centroids pam Classification pamr threshold
130 Negative Binomial Generalized Linear Model glm.nb Regression MASS link
131 Neural Network mxnet Classification, Regression mxnet layer1, layer2, layer3, learning.rate, momentum, dropout, activation
132 Neural Network mxnetAdam Classification, Regression mxnet layer1, layer2, layer3, dropout, beta1, beta2, learningrate, activation
133 Neural Network neuralnet Regression neuralnet layer1, layer2, layer3
134 Neural Network nnet Classification, Regression nnet size, decay
135 Neural Networks with Feature Extraction pcaNNet Classification, Regression nnet size, decay
136 Non-Convex Penalized Quantile Regression rqnc Regression rqPen lambda, penalty
137 Non-Informative Model null Classification, Regression None
138 Non-Negative Least Squares nnls Regression nnls None
139 Oblique Random Forest ORFlog Classification obliqueRF mtry
140 Oblique Random Forest ORFpls Classification obliqueRF mtry
141 Oblique Random Forest ORFridge Classification obliqueRF mtry
142 Oblique Random Forest ORFsvm Classification obliqueRF mtry
143 Optimal Weighted Nearest Neighbor Classifier ownn Classification snn K
144 Ordered Logistic or Probit Regression polr Classification MASS method
145 Parallel Random Forest parRF Classification, Regression e1071, randomForest, foreach, import mtry
146 partDSA partDSA Classification, Regression partDSA cut.off.growth, MPD
147 Partial Least Squares kernelpls Classification, Regression pls ncomp
148 Partial Least Squares pls Classification, Regression pls ncomp
149 Partial Least Squares simpls Classification, Regression pls ncomp
150 Partial Least Squares widekernelpls Classification, Regression pls ncomp
151 Partial Least Squares Generalized Linear Models plsRglm Classification, Regression plsRglm nt, alpha.pvals.expli
152 Patient Rule Induction Method PRIM Classification supervisedPRIM peel.alpha, paste.alpha, mass.min
153 Penalized Discriminant Analysis pda Classification mda lambda
154 Penalized Discriminant Analysis pda2 Classification mda df
155 Penalized Linear Discriminant Analysis PenalizedLDA Classification penalizedLDA, plyr lambda, K
156 Penalized Linear Regression penalized Regression penalized lambda1, lambda2
157 Penalized Logistic Regression plr Classification stepPlr lambda, cp
158 Penalized Multinomial Regression multinom Classification nnet decay
159 Penalized Ordinal Regression ordinalNet Classification ordinalNet, plyr alpha, criteria, link
160 Polynomial Kernel Regularized Least Squares krlsPoly Regression KRLS lambda, degree
161 Principal Component Analysis pcr Regression pls ncomp
162 Projection Pursuit Regression ppr Regression nterms
163 Quadratic Discriminant Analysis qda Classification MASS None
164 Quadratic Discriminant Analysis with Stepwise Feature Selection stepQDA Classification klaR, MASS maxvar, direction
165 Quantile Random Forest qrf Regression quantregForest mtry
166 Quantile Regression Neural Network qrnn Regression qrnn n.hidden, penalty, bag
167 Quantile Regression with LASSO penalty rqlasso Regression rqPen lambda
168 Radial Basis Function Kernel Regularized Least Squares krlsRadial Regression KRLS, kernlab lambda, sigma
169 Radial Basis Function Network rbf Classification, Regression RSNNS size
170 Radial Basis Function Network rbfDDA Classification, Regression RSNNS negativeThreshold
171 Random Ferns rFerns Classification rFerns depth
172 Random Forest ordinalRF Classification e1071, ranger, dplyr, ordinalForest nsets, ntreeperdiv, ntreefinal
173 Random Forest ranger Classification, Regression e1071, ranger, dplyr mtry, splitrule, min.node.size
174 Random Forest Rborist Classification, Regression Rborist predFixed, minNode
175 Random Forest rf Classification, Regression randomForest mtry
176 Random Forest by Randomization extraTrees Classification, Regression extraTrees mtry, numRandomCuts
177 Random Forest Rule-Based Model rfRules Classification, Regression randomForest, inTrees, plyr mtry, maxdepth
178 Regularized Discriminant Analysis rda Classification klaR gamma, lambda
179 Regularized Linear Discriminant Analysis rlda Classification sparsediscrim estimator
180 Regularized Logistic Regression regLogistic Classification LiblineaR cost, loss, epsilon
181 Regularized Random Forest RRF Classification, Regression randomForest, RRF mtry, coefReg, coefImp
182 Regularized Random Forest RRFglobal Classification, Regression RRF mtry, coefReg
183 Relaxed Lasso relaxo Regression relaxo, plyr lambda, phi
184 Relevance Vector Machines with Linear Kernel rvmLinear Regression kernlab None
185 Relevance Vector Machines with Polynomial Kernel rvmPoly Regression kernlab scale, degree
186 Relevance Vector Machines with Radial Basis Function Kernel rvmRadial Regression kernlab sigma
187 Ridge Regression ridge Regression elasticnet lambda
188 Ridge Regression with Variable Selection foba Regression foba k, lambda
189 Robust Linear Discriminant Analysis Linda Classification rrcov None
190 Robust Linear Model rlm Regression MASS intercept, psi
191 Robust Mixture Discriminant Analysis rmda Classification robustDA K, model
192 Robust Quadratic Discriminant Analysis QdaCov Classification rrcov None
193 Robust Regularized Linear Discriminant Analysis rrlda Classification rrlda lambda, hp, penalty
194 Robust SIMCA RSimca Classification rrcovHD None
195 ROC-Based Classifier rocc Classification rocc xgenes
196 Rotation Forest rotationForest Classification rotationForest K, L
197 Rotation Forest rotationForestCp Classification rpart, plyr, rotationForest K, L, cp
198 Rule-Based Classifier JRip Classification RWeka NumOpt, NumFolds, MinWeights
199 Rule-Based Classifier PART Classification RWeka threshold, pruned
200 Self-Organizing Maps xyf Classification, Regression kohonen xdim, ydim, user.weights, topo
201 Semi-Naive Structure Learner Wrapper nbSearch Classification bnclassify k, epsilon, smooth, final_smooth, direction
202 Shrinkage Discriminant Analysis sda Classification sda diagonal, lambda
203 SIMCA CSimca Classification rrcov, rrcovHD None
204 Simplified TSK Fuzzy Rules FS.HGD Regression frbs num.labels, max.iter
205 Single C5.0 Ruleset C5.0Rules Classification C50 None
206 Single C5.0 Tree C5.0Tree Classification C50 None
207 Single Rule Classification OneR Classification RWeka None
208 Sparse Distance Weighted Discrimination sdwd Classification sdwd lambda, lambda2
209 Sparse Linear Discriminant Analysis sparseLDA Classification sparseLDA NumVars, lambda
210 Sparse Mixture Discriminant Analysis smda Classification sparseLDA NumVars, lambda, R
211 Sparse Partial Least Squares spls Classification, Regression spls K, eta, kappa
212 Spike and Slab Regression spikeslab Regression spikeslab, plyr vars
213 Stabilized Linear Discriminant Analysis slda Classification ipred None
214 Stabilized Nearest Neighbor Classifier snn Classification snn lambda
215 Stacked AutoEncoder Deep Neural Network dnn Classification, Regression deepnet layer1, layer2, layer3, hidden_dropout, visible_dropout
216 Stochastic Gradient Boosting gbm Classification, Regression gbm, plyr n.trees, interaction.depth, shrinkage, n.minobsinnode
217 Subtractive Clustering and Fuzzy c-Means Rules SBC Regression frbs r.a, eps.high, eps.low
218 Supervised Principal Component Analysis superpc Regression superpc threshold, n.components
219 Support Vector Machines with Boundrange String Kernel svmBoundrangeString Classification, Regression kernlab length, C
220 Support Vector Machines with Class Weights svmRadialWeights Classification kernlab sigma, C, Weight
221 Support Vector Machines with Exponential String Kernel svmExpoString Classification, Regression kernlab lambda, C
222 Support Vector Machines with Linear Kernel svmLinear Classification, Regression kernlab C
223 Support Vector Machines with Linear Kernel svmLinear2 Classification, Regression e1071 cost
224 Support Vector Machines with Polynomial Kernel svmPoly Classification, Regression kernlab degree, scale, C
225 Support Vector Machines with Radial Basis Function Kernel svmRadial Classification, Regression kernlab sigma, C
226 Support Vector Machines with Radial Basis Function Kernel svmRadialCost Classification, Regression kernlab C
227 Support Vector Machines with Radial Basis Function Kernel svmRadialSigma Classification, Regression kernlab sigma, C
228 Support Vector Machines with Spectrum String Kernel svmSpectrumString Classification, Regression kernlab length, C
229 The Bayesian lasso blasso Regression monomvn sparsity
230 The lasso lasso Regression elasticnet fraction
231 Tree Augmented Naive Bayes Classifier tan Classification bnclassify score, smooth
232 Tree Augmented Naive Bayes Classifier Structure Learner Wrapper tanSearch Classification bnclassify k, epsilon, smooth, final_smooth, sp
233 Tree Augmented Naive Bayes Classifier with Attribute Weighting awtan Classification bnclassify score, smooth
234 Tree Models from Genetic Algorithms evtree Classification, Regression evtree alpha
235 Tree-Based Ensembles nodeHarvest Classification, Regression nodeHarvest maxinter, mode
236 Variational Bayesian Multinomial Probit Regression vbmpRadial Classification vbmp estimateTheta
237 Wang and Mendel Fuzzy Rules WM Regression frbs num.labels, type.mf
238 Weighted Subspace Random Forest wsrf Classification wsrf mtry

In this tutorial, I tried to incorporate 200+ Machine Learning Algorithms of caret Packages in R. Bookmark the writeup, so that you do not need to remember the parameters of these 200+ machine learning algorithm. Hope you have enjoyed the tutorial. If you want to get updated, like the facebook page https://www.facebook.com/LearningBigDataAnalytics and stay connected.

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