# The general approaches for improving a SVM-based classifier which is low precision and high recall

I built a SVM-based classifier against a data set, the precision is about 66% and the recall is about 88%. Generally, what are the options to tune the parameter that can increase the precision?

"...Since we will be dealing with very unbalanced numbers of positive and negative examples, we introduce cost factors C_+ and C_- to be able to adjust the cost of false positives vs. false negatives...".
Now, if you want just an out-of-the-box solution, the ratio of C_+ to C_- can be passed as the -j parameter to SVMLight.