I've been an ML practioner since 2009. I've used every method imaginable or popular, I think. With the exception of non-linear SVMs. Linear SVM => All good, just the hingle loss optimization. Non-linear SVM, a bit of overkill with basis expansion. Just too slow, or too complex a model?
My impression: SVMs are more of theoretical interest than practical interest. Yeah, learn your statistics. Loss functions. Additive models. Neural nets. Linear models. Decision trees, kNNs etc. SVM is more of a special interest, imho.
We can definitely learn a piece from such an experienced practitioner. Thanks for sharing, I think your intuition matches with the other experienced once in the comments.
My impression: SVMs are more of theoretical interest than practical interest. Yeah, learn your statistics. Loss functions. Additive models. Neural nets. Linear models. Decision trees, kNNs etc. SVM is more of a special interest, imho.