Naive Bayes Classifier with Attribute Weighting

Tree Augmented Naive Bayes Classifier with Attribute Weighting

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

Generalized Additive Model using Splines

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 `mgcv` package is fully loaded when this model is used.

This model creates predictions using the mean of the posterior distributions but sets some parameters specifically to zero based on the tuning parameter `sparsity`. For example, when `sparsity = .5`, only coefficients where at least half the posterior estimates are nonzero are used.

Bayesian Ridge Regression (Model Averaged)

This model makes predictions by averaging the predictions based on the posterior estimates of the regression coefficients. While it is possible that some of these posterior estimates are zero for non-informative predictors, the final predicted value may be a function of many (or even all) predictors.

Dynamic Evolving Neural-Fuzzy Inference System

Distance Weighted Discrimination with Polynomial Kernel

Distance Weighted Discrimination with Radial Basis Function Kernel

The package is no longer on CRAN but can be installed from the archive at  https://cran.r-project.org/src/contrib/Archive/elmNN/

Fuzzy Rules Using Genetic Cooperative-Competitive Learning and Pittsburgh

Ridge Regression with Variable Selection

The `prune` option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in `?mboost::mstop`. If pruning is not used, the ensemble makes predictions using the exact value of the `mstop` tuning parameter value.