I'm working with sklearn SVM and I have a problem.
When I run the method
sklearn.SVM.SVC.fit() using a database with only a few features (< 10) it takes a very long time. This is weird because, when I run the same method with the same database using all of the features (> 100) it takes just a few seconds.
I'm using a polynomial kernel and this problem only appears when the degree is >= 3. And it is also weird that if I use only a few features, but I add 50 features with 0s in all the samples, it works fine!
I have tried the following experiments:
fit()with all the features and any kernel degree. Works fine.
fit()with a few features and kernel degree <= 2. Works fine.
fit()with a few features and kernel degree >= 3. Very slow.
fit()with kernel degree = 3, a few features but adding 50 features with 0s in all the samples. Works fine!!
My question is, how can I solve this? Does my trick of adding features with 0s affect the classification?