Many lines separate two classes, but only one is best. A support vector machine finds the boundary with the widest safety margin.
SVM Maximum MarginLive
the widest possible street
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A support vector machine picks the separating line with the widest margin to the nearest points of each class — the support vectors. A fatter margin generalizes better to new data. Try to hand-tune the line to maximize the dashed gap. Educational tool.
Reading this result: This orientation misclassifies at least one point, so the margin is meaningless until the two classes are cleanly separated first.
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How it works
An SVM chooses the separating hyperplane that maximizes the distance to the nearest points of each class — the support vectors. A wider margin generalizes better to unseen data. Try tilting and shifting the line to open the widest possible gap. Educational tool.
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The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.