# What other predictive models should I consider

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)

• This seems kind of broad and unfocused; can you make your question more specific? Sep 5, 2014 at 2:31
• I rephrased my question. Basically as I'm not an experiencee user in regression models I'm asking for more experienced voices that could tell me for example: "hey for that data type I'd try YYY model as well". Sep 5, 2014 at 2:37
• linear regression, regression with regularization, regression with restricted cubic splines, Cubist, Boosted tree... Sep 5, 2014 at 2:50
• @jpcgandre that could be a lot of work! might just stick with boosted trees since your interest seems to be with machine learning. Cubist has limited literature base. Linear regression should be there if you're comparing models - but doesn't look to be where your interest lies. Sep 5, 2014 at 3:05
• Yes, indeed I was thinking in checking the last two models. Thanks for the tip! Sep 5, 2014 at 3:10