SVM

• SVC
• NuSVC
• LinearSVC
• LinearSVR

How to use the SVM module in real projects

The SVM module offers margin-based models for classification and regression with strong performance on medium-sized structured datasets.

Selection checklist

1. Use SVC or NuSVC for non-linear or margin-sensitive classification settings.
2. Use LinearSVC for large sparse feature spaces where linear boundaries are sufficient.
3. Use LinearSVR for regression with margin-based robustness to moderate noise.

Common implementation workflow

1. Start from a simple baseline in this module and evaluate on a holdout split.
2. Compare at least one alternative algorithm from this module before locking production defaults.
3. Pair model quality metrics with runtime constraints (latency, memory, bundle size).

Common search intents

• svm javascript
• linearsvc typescript
• support vector machine nodejs

Explore algorithms in this module

• LinearSVC
• LinearSVR
• NuSVC
• SVC

FAQ

What problem does SVM solve in JavaScript machine learning projects?

SVM helps teams implement production-ready ML workflows in browser and Node.js environments with a familiar scikit-learn-style API.

When should I choose SVM instead of other SVM algorithms?

Use SVM when it best matches your data shape, labeling strategy, and runtime constraints. Benchmark against at least one alternative in the same module before finalizing defaults.

Can I run SVM in both browser and Node.js with @kanaries/ml?

Yes. @kanaries/ml is designed for JavaScript and TypeScript runtimes across browser applications, server-side Node.js services, and edge-friendly workflows.