I'm running a model that uses Landsat spectral values as predictors in a support vector machine to predict percent canopy cover using field data.
The problem I have is that I only have ~70 points from the field representing canopy cover to use as training data. I have run the model using a 20+ band stack of predictors, but I am quite worried that the model will be overfit if I use this many predictor variables. The model results are satisfactory with this group of 20 predictors, but I don't know of a way to see predictor importance in SVM so I can provide fewer predictors to the model with those that are performing the best.
Should I be concerned that the model is overfit and reduce my number of predictors, or does the 1 for 10 rule of thumb not apply as much as it does for a linear model?
Any insight you could provide would be greatly appreciated!