Svms are, by default, linear models. The decision boundary in the Svm problem is linear and since it’s the max margin we may enjoy nice generalization properties (as you probably know).
You probably also know that decision tree boundaries are non Linear And piecewise. It’s not so straightforward to find splits on continuous features.
Ie If the data is linearly separable then why not. Even using hinge loss with nns is not uncommon.
You probably see gbms winning a lot of competitions compared to svms because a lot of competitions may have a lot of data and non linear decision boundaries. some problems don’t have these characteristics.
You probably also know that decision tree boundaries are non Linear And piecewise. It’s not so straightforward to find splits on continuous features.
Ie If the data is linearly separable then why not. Even using hinge loss with nns is not uncommon.
You probably see gbms winning a lot of competitions compared to svms because a lot of competitions may have a lot of data and non linear decision boundaries. some problems don’t have these characteristics.