How to choose parameters for svm in sklearn
Yes, this is mostly a matter of experimentation -- especially as you've told us very little about your data set: separability, linearity, density, connectivity, ... all the characteristics that affect classification algorithms.
Try the linear and Gaussian kernels for starters.  If linear doesn't work well and Gaussian does, then try the other kernels.
Once you've found the best 1 or 2 kernels, then play with the cost and gamma parameters.  Gamma is a "slack" parameter: it gives the kernel permission to make a certain proportion of raw classification errors as a trade-off for other benefits: width of the gap, simplicity of the partition function, etc.
I haven't yet had an application that got more than trivial benefit from altering the cost.
I'm trying to use SVM from sklearn for a classification problem. I got a highly sparse dataset with more than 50K rows and binary outputs.
The problem is I don't know quite well how to efficiently choose the parameters, mainly the kernel, gamma anc C.   
For the kernels for example, am I supposed to try all kernels and just keep the one that gives me the most satisfying results or is there something related to our data that we can see in the first place before choosing the kernel ?
Same goes for C and gamma.
Thanks !