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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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- r_squared |
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- mse |
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tags: |
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- knn |
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- nearest-neighbors |
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- tabular |
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- classification |
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- regression |
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- cpu |
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- low-latency |
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- ann |
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- distance-weighted |
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- production-ready |
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--- |
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--- |
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language: |
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- en |
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tags: |
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- tabular |
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- nearest-neighbors |
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- knn |
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- classification |
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- regression |
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- cpu |
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- low-latency |
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- interpretable |
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library_name: smart-knn |
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license: mit |
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pipeline_tag: tabular-classification |
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model_name: SmartKNN v2 |
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--- |
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# SmartKNN v2 |
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**SmartKNN v2** is a high-performance, CPU-first nearest-neighbors model designed for **low-latency production inference** on real-world tabular data. |
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It delivers **competitive accuracy with gradient-boosted models** while maintaining **sub-millisecond single-prediction latency (p95)** on CPU-only systems. |
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SmartKNN v2 is part of the **SmartEco** ecosystem. |
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--- |
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## Model Details |
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- **Model type:** Distance-weighted K-Nearest Neighbors |
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- **Tasks:** Classification, Regression |
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- **Backend:** Adaptive (Brute-force + ANN) |
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- **Hardware:** CPU-only (GPU not required) |
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- **Focus:** Low latency, interpretability, production readiness |
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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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--- |
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## What’s New in v2 |
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- Full classification support restored |
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- ANN backend introduced for scalable neighbor search |
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- Automatic backend selection (small → brute, large → ANN) |
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- Distance-weighted voting for improved accuracy |
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- Interpretable neighbor influence statistics |
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- Foundation for adaptive-K strategies |
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--- |
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## Architecture Overview |
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- Feature Weighting |
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- Backend Selector |
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- Brute Backend (small datasets) |
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- ANN Backend (large datasets) |
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- Distance Kernel |
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- Weighted Voting |
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- Prediction |
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This hybrid architecture ensures consistent low latency across dataset sizes. |
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--- |
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## Performance (Internal Evaluation) |
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> Public benchmarks will be released soon. |
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From internal testing on real-world tabular datasets: |
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- Accuracy comparable to XGBoost / LightGBM / CatBoost |
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- Single-prediction latency: |
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- Median: sub-millisecond |
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- p95: consistently low on CPU |
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- Predictable batch inference scaling |
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SmartKNN v2 has **not yet reached its performance ceiling**. Future releases will further optimize speed and accuracy. |
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--- |
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## Limitations |
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- Not designed for unstructured data (text, images) |
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- ANN backend focuses on CPU efficiency, not GPU acceleration |
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- Best suited for tabular datasets |
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--- |
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## Future Work |
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- Adaptive-K accuracy optimization |
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- Kernel-level speed improvements |
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- Custom ANN backend |
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## Links |
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- Website: https://thatipamula-jashwanth.github.io/SmartEco/ |
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- Source Code: https://github.com/thatipamula-jashwanth/smart-knn |
