| --- |
| license: mit |
| language: |
| - en |
| metrics: |
| - accuracy |
| - f1 |
| - r_squared |
| - mse |
| tags: |
| - knn |
| - nearest-neighbors |
| - tabular |
| - classification |
| - regression |
| - cpu |
| - low-latency |
| - ann |
| - distance-weighted |
| - production-ready |
| --- |
| --- |
| 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 |
| --- |
| |
| # 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. |
| |
| --- |
| |
| ## 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. |
| |
| --- |
| |
| ## 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 |
| |
| --- |
| |
| ## 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. |
| |
| --- |
| |
| ## 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. |
| |
| --- |
| |
| ## Limitations |
| |
| - Not designed for unstructured data (text, images) |
| - ANN backend focuses on CPU efficiency, not GPU acceleration |
| - Best suited for tabular datasets |
| |
| --- |
| |
| ## 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 |
