SmartKNN v2.2: Improving Scalability, Correctness, and Training Speed

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SmartKNN v2.2 is a focused update aimed at making the library more scalable, predictable, and efficient when working with large datasets. While this is a minor version bump, the release introduces meaningful internal improvements that directly impact training-time performance and backend correctness especially at scale
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This update does not change the public API or inference behavior, making it a safe upgrade for existing users.

Smarter Feature Weighting at

Feature weighting based on Mutual Information (MI) plays a critical role in SmartKNN’s performance. In v2.2, MI computation has been optimized to better handle very high-dimensional datasets.

The key improvement is parallelized MI computation, which significantly reduces training time when the number of features is large. Importantly, the behavior for low- and medium-dimensional datasets remains unchanged, ensuring consistency and reproducibility for existing workflows.

Correct Automatic Backend Selection

SmartKNN supports multiple backends, including brute-force and ANN-based approach. In earlier versions, automatic backend selection could introduce unnecessary overhead for small datasets.

In v2.2, this logic has been corrected:

The brute-force backend is now explicitly enforced below 10K rows
ANN backends are avoided when they provide no practical benefit

This change improves correctness, reduces setup overhead, and ensures the most appropriate backend is used by default.

More Stable Feature Selection

Feature selection has been refined with updates to the Random Forest–based feature relevance logic. Improved split constraints make feature pruning more stable, particularly when dealing with noisy or skewed data distributions.

The result is more reliable feature selection without increasing model complexity or changing user-facing behavior.

Faster ANN Training for Very Large Datasets

For users working at scale, ANN index construction can be a major bottleneck. SmartKNN v2.2 introduces internal optimizations that significantly improve ANN training performance on multi-million-row datasets.

These changes:

Improve overall scalability
Reduce ANN index build time

Inference accuracy remain unchanged.

Measured Performance Improvement

Across internal benchmarks, the following training-time improvements were observed:

Around 10% faster training on medium-sized datasets
Up to 25% faster training on multi-million-row datasets
Reduced ANN index build overhead for large-scale workloads

No regressions were observed in inference accuracy...

Improved Robustness During Inference

This release also fixes inference-time handling of NaN and Inf values in query inputs. SmartKNN now consistently emits a warning when invalid values are detected, while preserving existing normalization and prediction behavior.

This makes inference safer and easier to debug in real-world pipelines.