SmartKNN_v2

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language:

• en tags:
• tabular
• nearest-neighbors
• knn
• classification
• regression
• cpu
• low-latency
• interpretable library_name: smart-knn license: mit pipeline_tag: tabular-classification model_name: SmartKNN v2

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SmartKNN v2

SmartKNN v2 is a high-performance, CPU-first nearest-neighbors model designed for low-latency production inference on real-world tabular data.

It delivers competitive accuracy with gradient-boosted models while maintaining sub-millisecond single-prediction latency (p95) on CPU-only systems.

SmartKNN v2 is part of the SmartEco ecosystem.

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Model Details

• Model type: Distance-weighted K-Nearest Neighbors
• Tasks: Classification, Regression
• Backend: Adaptive (Brute-force + ANN)
• Hardware: CPU-only (GPU not required)
• Focus: Low latency, interpretability, production readiness

Unlike classical KNN, SmartKNN v2 learns feature importance, adapts execution strategy based on data size, and uses optimized distance kernels for fast inference.

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What’s New in v2

• Full classification support restored
• ANN backend introduced for scalable neighbor search
• Automatic backend selection (small → brute, large → ANN)
• Distance-weighted voting for improved accuracy
• Interpretable neighbor influence statistics
• Foundation for adaptive-K strategies

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Architecture Overview

• Feature Weighting
• Backend Selector
• Brute Backend (small datasets)
• ANN Backend (large datasets)
• Distance Kernel
• Weighted Voting
• Prediction

This hybrid architecture ensures consistent low latency across dataset sizes.

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Performance (Internal Evaluation)

Public benchmarks will be released soon.

From internal testing on real-world tabular datasets:

• Accuracy comparable to XGBoost / LightGBM / CatBoost
• Single-prediction latency:
  • Median: sub-millisecond
  • p95: consistently low on CPU
• Predictable batch inference scaling

SmartKNN v2 has not yet reached its performance ceiling. Future releases will further optimize speed and accuracy.

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Limitations

• Not designed for unstructured data (text, images)
• ANN backend focuses on CPU efficiency, not GPU acceleration
• Best suited for tabular datasets

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Future Work

• Adaptive-K accuracy optimization
• Kernel-level speed improvements
• Custom ANN backend

Links

• Website: https://thatipamula-jashwanth.github.io/SmartEco/
• Source Code: https://github.com/thatipamula-jashwanth/smart-knn