Over the past few weeks, I’ve been building SmartKNN - a practical take on the classic K-Nearest Neighbors algorithm, focused on real-world usability rather than theory.
This week, the project crossed 520 installs in about 20 days.
It’s a small milestone, but an encouraging signal that the problem resonates with others too.
Why this milestone matters (to me)
The number itself isn’t huge. What matters is what it represents.
KNN is one of the simplest and most interpretable ML algorithms, but in practice it often gets abandoned because:
- It becomes slow at scale
- It struggles in high-dimensional spaces
- It’s treated as a baseline, not a production model
Seeing consistent installs tells me that there are still people looking for simple, explainable models - but with modern performance expectations.
What SmartKNN tries to improve
SmartKNN doesn’t try to compete with tree ensembles or deep models.
Its goal is simpler: make KNN usable again in production-like settings.
At a high level, it focuses on:
- Built-in feature selection to reduce noise
- Distance-weighted voting instead of naive majority voting
- Optional ANN backends for faster neighbour retrieval
- Safe defaults so it works without heavy preprocessing
The idea is not “fancier KNN”, but more practical KNN.
What didn’t work early on
The first versions weren’t great.
- Feature weighting was unstable
- Benchmarks were misleading
- Some optimizations helped speed but hurt accuracy
A lot of early work was rewriting and simplifying rather than adding features.
That process taught me that boring improvements usually matter more than clever ones.
What’s next
The current focus is on:
- Locking SmartKNN v2 without breaking changes
- Running more public, reproducible benchmarks
It’s still early, and the project is evolving slowly and deliberately.
Closing thoughts
This post isn’t a launch announcement - it’s just a checkpoint while building in public.
If you’ve ever tried to use KNN in production (or decided not to), I’d genuinely love to hear what broke for you.
Feedback - even critical - is always welcome.
If you’re curious, SmartKNN is open-source and available on PyPI and GitHub.
Trying it on a dataset you already know is often the fastest way to see whether it fits your workflow.
pip install smart-knn
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