I have a numeric dataset involving 34 input variables and one output variable.
I'd like to fit a model to the data so to be able to make predictions (regression problem).
So far I've tested
MARS (Multivariate Adaptive Regression Spline, earth package), simple random forest (Random Forest, randomForest package), neural networks (Model Averaged Neural Network, package nnet), support vector machines (kernlab package, polynomial, RBF, laplace kernels). I'm using R.
The best model (both in train and test data sets) is the Laplace kernel (R2 = 89%) but the SVR polynomial (degree = 2), the RBF and the neural networks are close seconds (with 87-88%) and the random forest with 85% and MARS with 82%.
Do you suggest any other model that I could try to fit to my data set, based on the results I've fgot so far and on the type (numeric) and size (700 values for each variable) of the data set? Thanks
I'd like to predict R (last column of data set)