Bayesian Regularized Neural Networks
method = 'brnn'
Type: Regression
Tuning Parameters: neurons (# Neurons)
Extreme Learning Machine
method = 'elm'
Type: Classification, Regression
Tuning Parameters: nhid (#Hidden Units), actfun (Activation Function)
Model Averaged Neural Network
method = 'avNNet'
Type: Classification, Regression
Tuning Parameters: size (#Hidden Units), decay (Weight Decay), bag (Bagging)
Multi-Layer Perceptron
method = 'mlp'
Type: Regression, Classification
Tuning Parameters: size (#Hidden Units)
Multi-Layer Perceptron
method = 'mlpWeightDecay'
Type: Regression, Classification
Tuning Parameters: size (#Hidden Units), decay (Weight Decay)
Neural Network
method = 'neuralnet'
Type: Regression
Tuning Parameters: layer1 (#Hidden Units in Layer 1), layer2 (#Hidden Units in Layer 2), layer3 (#Hidden Units in Layer 3)
Neural Network
method = 'nnet'
Type: Classification, Regression
Tuning Parameters: size (#Hidden Units), decay (Weight Decay)
Neural Networks with Feature Extraction
method = 'pcaNNet'
Type: Classification, Regression
Tuning Parameters: size (#Hidden Units), decay (Weight Decay)
Penalized Multinomial Regression
method = 'multinom'
Type: Classification
Tuning Parameters: decay (Weight Decay)
Quantile Regression Neural Network
method = 'qrnn'
Type: Regression
Tuning Parameters: n.hidden (#Hidden Units), penalty ( Weight Decay), bag (Bagged Models?)
Radial Basis Function Network
method = 'rbf'
Type: Classification
Tuning Parameters: size (#Hidden Units)
Radial Basis Function Network
method = 'rbfDDA'
Type: Regression, Classification
Tuning Parameters: negativeThreshold (Activation Limit for Conflicting Classes)