SmartKNN
A modern, weighted nearest-neighbor learning algorithm with learned feature importance and adaptive neighbor search.
Overview
SmartKNN is a nearest-neighbor–based learning method that belongs to the broader KNN family of algorithms.
It is designed to address common limitations observed in classical KNN approaches, including:
uniform treatment of all features
sensitivity to noisy or weakly informative dimensions
limited scalability as dataset size grows
SmartKNN incorporates data-driven feature importance estimation, dimension suppression, and adaptive neighbor search strategies. Depending on dataset characteristics, it can operate using either a brute-force search or an approximate nearest-neighbor (ANN) backend, while exposing a consistent, scikit-learn–compatible API.
The method supports both regression and classification tasks and prioritizes robustness, predictive accuracy, and practical inference latency across a range of dataset sizes.
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