I'm trying to program an SVM in Python to categorize proteins as "Go" or "No-Go".
I have a list of about 30 proteins, each with ~ 100 columns of structure-related parameters and 1 column of "True" or "False". I will use this as my training set.
I don't want to write an SVM that uses all 100 columns of data. How do I identify which of the columns impact the output? Things I've looked at, albeit briefly:
correlation, linear regression, multiple regression, nonparametric regression
..but all of these seem overly complicated because they're intended to predict non-discrete dependent values (e.g. average height or dollar-cost) instead of a binary result (True | False).
Although I appreciate any guidance, I'm really just looking for the correct vocabulary so that I can research my problem without flailing in the dark.