Find classification margins for support vector machine (SVM) classifier

`m = margin(SVMModel,TBL,ResponseVarName)`

`m = margin(SVMModel,TBL,Y)`

`m = margin(SVMModel,X,Y)`

returns the classification
margins (`m`

= margin(`SVMModel`

,`TBL`

,`ResponseVarName`

)`m`

) for the trained support vector
machine (SVM) classifier `SVMModel`

using the sample data in
table `TBL`

and the class labels in
`TBL.ResponseVarName`

.

`m`

is returned as a numeric vector with the same length as
`Y`

. The software estimates each entry of
`m`

using the trained SVM classifier
`SVMModel`

, the corresponding row of
`X`

, and the true class label
`Y`

.

For binary classification, the software defines the margin for
observation *j*, *m _{j}*, as

$${m}_{j}=2{y}_{j}f({x}_{j}),$$

where *y _{j}* ∊ {-1,1}, and

[1] Christianini, N., and J. C. Shawe-Taylor. *An
Introduction to Support Vector Machines and Other Kernel-Based Learning
Methods*. Cambridge, UK: Cambridge University Press, 2000.

`ClassificationSVM`

| `CompactClassificationSVM`

| `edge`

| `fitcsvm`

| `loss`

| `predict`