According to the Scikit-Learn documentation for the RBF kernel: The length scale of the kernel. If a float, an isotropic kernel is used. If an array, an anisotropic kernel is used where each dimension of l defines the length-scale of the respective feature dimension.
I am currently working on a problem where I am setting the length-scale of each individual feature (which I assume is synonymous with dimension here). My understanding is that a smaller length scale implies a more complex function.
My question is, can I use this parameter to explain how well a certain feature will help a model generalize to new data?
For example, if I have a data set which, after optimizing the length-scale value looks like this: [Feature_1: length-scale = 20] [Feature_2: length-scale = 1] [Feature_3: length-scale = 5]
Does this mean that, if I had to pick one feature which would help a model generalize to new data, it would be Feature_1? Is Feature_2 potentially causing my model to overfit? Are these fair assumptions to make?
Note: I am using support vector regression with this kernel.