seniorSVM
How does SVM rank feature importance?
Updated May 17, 2026
Short answer
In linear SVM, feature importance is derived from the magnitude of coefficients.
Deep explanation
The absolute value of weights in w vector indicates feature contribution. Larger magnitude means higher influence on decision boundary. However, this interpretation is not valid for nonlinear kernels.
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