supervised learning models used for classification and regression analysis.
An SVM model is a representation of the training examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible.
Non-probabilistic binary linear classifier (some methods exist to use SVM in a probabilistic classification setting). Can be made non-linear with the "kernel trick" (implicitly mapping the inputs into high-dimensional feature spaces.)
Visualising Top Features in Linear SVM with Scikit Learn and Matplotlib(About) > The weights obtained from svm.coef_ represent the vector coordinates which are orthogonal to the hyperplane and their direction indicates the predicted class. The absolute size of the coefficients in relation to each other can then be used to determine feature importance for the data separation task