I'm building a model based on a database with around 90000 observations and 100+ variables. My target variable is binary (0 or 1).
I'm using SAS Miner and I want to test a few high performance techniques.
I already tested Random Forest and got very successful results.
Now, I'm giving a try at using Support Vector Machine (SVM) and Radial Basis Function (RBF). However, I'm experiencing a lot of issues running those procedures those due to insufficient memory.
I researched a bit about both procedures but I still have no clue if It's possible to run SVM and RBF with such large data.
If that's not possible, when should I use SVM and RBF?
Thanks in advance!
Edit 1: I was able to run SVM with 22000 observations database and a small amount of variables (five). Still, no success in performing with the bigger data and RBF.