A few days ago I shared SmartKNN. I’ve now added full benchmark results.
I ran regression and classification benchmarks on multiple OpenML datasets. Everything was run on Kaggle CPU (no GPU).
What I measured:
1. 3-fold CV metrics
2. Test metrics
3. Single inference P95 latency
All models were run with default settings. No hyperparameter tuning...
1. Preprocessing was consistent across models-
Median imputation for missing values
2. Target encoding for categorical features (done fold-wise to avoid leakage)
3. Scaling applied only to KNN and Linear models
No dataset-specific tricks...
The goal here was to understand the practical tradeoffs of a weighted KNN under real CPU constraints, especially when you measure single
Full benchmark tables and implementation details are in the repo.. https://github.com/thatipamula-jashwanth/smart-knn
If anyone has suggestions for-
More datasets to test, Additional baselines to compare, Improvements to the benchmark setup, Better ways to measure latency, I’d appreciate the feedback.
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[ 463 ms ] story [ 558 ms ] threadI ran regression and classification benchmarks on multiple OpenML datasets. Everything was run on Kaggle CPU (no GPU).
What I measured: 1. 3-fold CV metrics 2. Test metrics 3. Single inference P95 latency
All models were run with default settings. No hyperparameter tuning...
1. Preprocessing was consistent across models- Median imputation for missing values 2. Target encoding for categorical features (done fold-wise to avoid leakage) 3. Scaling applied only to KNN and Linear models No dataset-specific tricks...
The goal here was to understand the practical tradeoffs of a weighted KNN under real CPU constraints, especially when you measure single
Full benchmark tables and implementation details are in the repo.. https://github.com/thatipamula-jashwanth/smart-knn If anyone has suggestions for- More datasets to test, Additional baselines to compare, Improvements to the benchmark setup, Better ways to measure latency, I’d appreciate the feedback.