Neighbors

• KNearstNeighbors
• BallTree
• KDTree

How to use the Neighbors module in real projects

The Neighbors module supports instance-based learning and efficient nearest-neighbor search for recommendations, retrieval, and similarity tasks.

Selection checklist

1. Use KNearstNeighbors when you need a direct non-parametric classifier/regressor baseline.
2. Use KDTree or BallTree to speed up nearest-neighbor queries for repeated lookup workloads.
3. Tune distance metrics and neighborhood size based on recall/precision targets for your product.

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

• knn javascript
• kdtree typescript
• nearest neighbor search browser

Explore algorithms in this module

• ballTree
• kdTree
• knn

FAQ

What problem does Neighbors solve in JavaScript machine learning projects?

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

When should I choose Neighbors instead of other Neighbors algorithms?

Use Neighbors 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 Neighbors 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.