SmartKNN
SmartKNN is a modern, weighted nearest-neighbors system designed for fast, interpretable, CPU-first machine learning inference.
It extends classical KNN into a production-oriented system by learning feature importance, adapting distance computation, and selecting execution strategies based on scale and latency constraints.
SmartKNN is built as a system, not a toy algorithm - with explicit control over performance, behavior, and engineering trade-offs.
What Problem Does SmartKNN Solve?
Traditional KNN has well-known strengths: - Simple and transparent decision logic - Strong performance on many real-world datasets - Natural interpretability
However, it breaks down in production due to: - Poor scalability at inference time - Uniform treatment of all features - Unpredictable latency under load - Limited control over distance behavior
SmartKNN addresses these limitations while preserving the core advantages of nearest-neighbor learning.
Core Capabilities
Learned Feature Importance
SmartKNN learns how important each feature is for prediction, reducing noise and improving decision quality without manual tuning.
Feature Pruning Engine
Weak or irrelevant features can be explicitly down-weighted or excluded, enforcing stability and improving both performance and latency predictability.
Adaptive Distance Engine
Distance computation is treated as a first-class component, allowing similarity behavior to adapt to the problem rather than relying on fixed metrics.
Backend Strategy
SmartKNN supports multiple execution backends - including brute-force and approximate nearest-neighbor strategies - while preserving consistent prediction semantics and APIs.
CPU-Optimized Inference
Inference is optimized for commodity CPUs, with a focus on predictable mean and tail latency.
Interpretable Predictions
Predictions are explainable in terms of neighbor influence, feature contributions, and distance behavior.
How SmartKNN Fits into SmartEco
SmartKNN is the primary production-ready system within the SmartEco ecosystem.
It serves as: - A reference implementation for SmartEco’s production engineering standards - A stable system for real-world deployment - A foundation for ideas that may later migrate into other systems
SmartKNN prioritizes reliability, transparency, and operational clarity over experimental novelty.
Evaluation and Benchmarks
SmartKNN is evaluated using transparent and reproducible methodologies focused on real-world constraints.
Benchmarks emphasize: - Inference latency, including tail behavior - Stability under sustained load - Accuracy and recall trade-offs
Detailed benchmark results and evaluation methodology are documented separately.
Documentation and Usage
To explore SmartKNN in depth: - Read the documentation for design details and algorithms - Review installation and quickstart guides - Examine limitations and recommended use cases before deployment
SmartKNN follows a documented versioning strategy to ensure stability as the system evolves.
Get Started
- Read the SmartKNN Documentation
- View Benchmarks
- Browse the Source Code
SmartKNN is actively developed as part of the SmartEco ecosystem, with a focus on building a nearest-neighbors system that is practical, understandable, and production-ready.