KNN Decoder

The KNN Decoder interprets model decisions by exposing how a query vector is positioned relative to labeled example points. It accepts a feature space vector, a labeled dataset, a value for k, and a distance metric, returning the k nearest neighbors with their labels, feature distances, and optional weights. This enables developers, data scientists, and ML engineers to inspect locality factors that drive classification or regression outcomes. The tool supports both flat and distance-weighted voting schemes and can operate on numeric embeddings or engineered feature arrays.

Conceptually, the tool constructs a neighbor set by computing distances between the query and each dataset point, then sorts by distance. It aggregates neighbor labels to reveal dominant classes, mixed decisions, or ties. For explainability, it can present basic statistics such as average distance to each label, consensus score, and a confidence measure. It is designed to help validate model behavior, compare distance metrics (Euclidean, Manhattan, cosine), and debug mislabeled instances without modifying model code.

Core value rests in transparency, reproducibility, and faster debugging. Key features include input validation, optional normalization, support for multiple datasets, batch decoding, and export of results as JSON or CSV. Typical use cases include feature engineering evaluation, model auditing in classification pipelines, and rapid prototyping of distance-based retrieval tasks. The differentiator is a compact decoding workflow that emphasizes explainability and auditability for ML practitioners working with embedding spaces and kNN-based decisions.