When I first learned about KNN, I assumed the implementation in scikit-learn was essentially the model. It felt “solved.” You pick k, choose a distance metric, maybe normalize the data, and you’re done.<p>Then I started asking a simple question: why can’t nearest neighbor methods be both fast and competitive with stronger tabular models in real production settings?<p>That question led me down a much deeper path than I expected.<p>First, I realized there isn’t just “KNN.” There are many variations: weighted distances, metric learning, approximate search structures, indexing strategies, pruning heuristics, and hybrid pipelines. I also discovered that most fast approaches trade accuracy for speed, and many accurate ones assume large training time, heavy indexing, or GPU-based vector engines.<p>I wanted something CPU-focused, predictable, and deployable.<p>Some of the key things I learned along the way:<p>Feature importance matters a lot more than I initially thought. Treating all features equally is one of the biggest weaknesses of classical KNN. Noise and irrelevant dimensions directly hurt distance quality.<p>The curse of dimensionality is not theoretical — it’s painfully practical. In high dimensions, naive distance metrics degrade quickly.<p>Scaling and normalization are not optional details. They fundamentally shape the geometry of the space.<p>Inference time often matters more than raw accuracy. In many real-world systems, predictable latency is more valuable than squeezing out 0.5% extra accuracy.<p>Memory footprint is a first-class concern. Nearest neighbor methods store the dataset; this forces you to think carefully about representation and pruning.<p>GBMs are not “just models.” They’re systems. After studying gradient boosting more closely, I started seeing it less as a single model and more as a structured system with layered feature selection, residual fitting, and region partitioning. That perspective changed how I thought about improving KNN.<p>I began experimenting with:<p>Learned feature weighting to reduce noise.<p>Feature pruning to reduce dimensional effects.<p>Vectorized distance computation on CPU.<p>Integrating approximate neighbor search while preserving final exact scoring.<p>Structuring the algorithm more like a deployable system rather than a classroom algorithm.<p>One big realization: no model dominates under every dataset and constraint. There is no universal winner. Performance depends heavily on feature quality, data size, dimensionality, and latency requirements.<p>Building this forced me to think less about “which algorithm is best” and more about:<p>What constraints does production impose?<p>Where is the real bottleneck: compute, memory, or data geometry?<p>How do we balance accuracy, latency, and simplicity?<p>I’m still exploring this space and would really appreciate feedback from people who’ve worked on large-scale similarity search or production ML systems.<p>If anyone has suggestions on:<p>Better CPU vectorization strategies,<p>Lessons from deploying nearest-neighbor systems at scale,<p>Or papers I should study on metric learning / scalable distance methods,<p>I’d love to learn more.<p>I’ve put the current implementation on GitHub for anyone curious, but I’m mainly interested in discussion and technical feedback.
Hello, ChatGPT ;)<p>I found the benchmarks, but I'm having some trouble making sense of them. Sounds like this project would benefit from some graphs. And maybe some examples of real-world usecases, and how the different approaches stack up there?