SemiSupervised
Learn how to use SemiSupervised algorithms in @kanaries/ml for JavaScript and TypeScript machine learning projects.
How to use the SemiSupervised module in real projects
The SemiSupervised module helps when labeled data is limited but unlabeled samples are abundant.
Selection checklist
- Use LabelPropagation for graph-style transductive learning when neighborhood structure is reliable.
- Use LabelSpreading for smoother label diffusion with stronger regularization.
- Benchmark against a supervised-only baseline to confirm unlabeled data is adding value.
Common implementation workflow
- Start from a simple baseline in this module and evaluate on a holdout split.
- Compare at least one alternative algorithm from this module before locking production defaults.
- Pair model quality metrics with runtime constraints (latency, memory, bundle size).
Common search intents
semi supervised learning javascriptlabel propagation typescriptlow label ml nodejs
Explore algorithms in this module
FAQ
What problem does SemiSupervised solve in JavaScript machine learning projects?
SemiSupervised helps teams implement production-ready ML workflows in browser and Node.js environments with a familiar scikit-learn-style API.
When should I choose SemiSupervised instead of other SemiSupervised algorithms?
Use SemiSupervised 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 SemiSupervised 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.