K-Nearest Neighbors makes a prediction from nearby stored samples instead of fitting a gradient-based model. This makes it ideal for learning how distance, neighborhood size, and local structure affect a decision.
Open the interactive visualizer →For a query point, KNN calculates distances to training samples, selects the nearest K points, and combines their labels or values according to the configured weighting rule.
The main visualizer runs the implemented model in your browser and exposes the stored state, predictions, and metrics instead of substituting fabricated demo numbers.
Start with the default educational dataset, predict what will happen, run the model, then inspect one calculation or parameter at a time. For supported CSV workflows, local files are parsed and processed in the browser rather than uploaded by the application.