Visualizing Support Vector Machines (SVM) with Multiple Explanatory Variables I was wondering if anyone was aware of any methods for visualizing an SVM model where there are more than three continuous explanatory variables. In my particular situation, my response variable is binomial, with 6 continuous explanatory variables (predictors), one categorical explanatory variable (predictor). I have already reduced the number of predictors and I am primarily using R for my analysis.

(I am unaware if such a task is possible/ worth pursuing.)

Thanks for your time.
Does it matter that the model is created in the form of SVM?

If no, I have seen a clever 6-D visualization. Its varieties are becoming popular in medical presentations. 
3 dimensions are shown as usual, in orthographic projection.
Dimension 4 is color (0..255)
Dimension 5 is thickness of the symbol 
Dimension 6 requires animation. It is a frequency of vibration of a dot on the screen. 
In static, printed versions, one can replace frequency of vibration by blur around the point, for a comparable visual perception.

If yes, and you specifically need to draw separating hyperplanes, and make them look like lines\planes, the previous trick will not produce good results. Multiple 3-D images are better.