Support Vector Machine (Classification)
Also known as: Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.
Key highlights
- Effective in high-dimensional feature spaces, even when there are more features than the boundary's complexity would suggest.
- The maximum-margin principle and the use of only support vectors give robust, generalisable boundaries.
- Kernels let it capture nonlinear class boundaries without manually engineering new features.
- Assumption-light: it does not require normally distributed data.
Intuition
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How it works
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When to use it
Use SVM for binary or categorical classification on tabular data with roughly 50 or more observations, especially when the feature space is high-dimensional and predictive accuracy matters more than explicit coefficients. It does not require normally distributed data, but features must be standardised and the kernel and C must be tuned by cross-validation. On very small samples, prefer a simpler classifier such as logistic regression or naive Bayes.
Strengths & limitations
- Effective in high-dimensional feature spaces, even when there are more features than the boundary's complexity would suggest.
- The maximum-margin principle and the use of only support vectors give robust, generalisable boundaries.
- Kernels let it capture nonlinear class boundaries without manually engineering new features.
- Assumption-light: it does not require normally distributed data.
- Sensitive to the choice of kernel and the C parameter, which must be tuned by cross-validation.
- Requires careful feature standardisation; unscaled features distort the margin.
- Becomes unstable on small samples, where there are too few support vectors to define a reliable boundary.
- Provides no directly interpretable coefficients and no native class-probability estimates.
Common pitfalls
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Applications
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Frequently asked
Why do I have to standardise the features?
SVM measures distances to the separating hyperplane, so features on larger numeric scales dominate the margin. Standardising every feature (StandardScaler) puts them on comparable scales so the boundary reflects the true class structure rather than arbitrary units.
What does the C parameter control?
C trades off margin width against classification errors. A large C forces a tight fit to the training data with a narrow margin; a small C allows more margin violations in exchange for a wider, smoother boundary. The right value is found by cross-validation.
What is the kernel for?
When the classes cannot be separated by a straight boundary, a kernel (such as the radial basis function) implicitly maps the data into a higher-dimensional space where a separating hyperplane exists, letting SVM model nonlinear boundaries. The kernel choice should be validated by cross-validation.
When should I not use SVM?
On very small samples — below about 50 observations — there are too few support vectors to define a stable boundary and there is not enough data to tune the kernel and C, so the model risks overfitting. In that case a simpler classifier such as logistic regression or naive Bayes is preferable.
Sources
- 1.Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
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Cite this page
ScholarGate. (2026, June 1). Support Vector Machine. ScholarGate. https://scholargate.app/machine-learning/svm-classification