Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion. Unlike other packages used by `train`, the `gam` package is fully loaded when this model is used.

Gaussian Process with Radial Basis Function Kernel

Genetic Lateral Tuning and Rule Selection of Linguistic Fuzzy Systems

Negative Binomial Generalized Linear Model

Generalized Linear Model with Stepwise Feature Selection

Dimension of the Discriminative Subspace

Polynomial Kernel Regularized Least Squares

Radial Basis Function Kernel Regularized Least Squares

Linear Regression with Backwards Selection

Linear Regression with Forward Selection

Linear Regression with Stepwise Selection

Unlike other packages used by `train`, the `logicFS` package is fully loaded when this model is used.

Least Squares Support Vector Machine with Polynomial Kernel

Least Squares Support Vector Machine with Radial Basis Function Kernel

Maximum Uncertainty Linear Discriminant Analysis

Multilayer Perceptron Network with Weight Decay

After `train` completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use  `keras::unsearlize_model(object$finalModel$object)` in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Unlike other packages used by `train`, the `dplyr` package is fully loaded when this model is used.

After `train` completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use  `keras::unsearlize_model(object$finalModel$object)` in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Finally, the cost parameter weights the first class in the outcome vector. Unlike other packages used by `train`, the `dplyr` package is fully loaded when this model is used.