u/depr001

See how different classification and clustering approaches behave on the same two-dimensional data. I used a handful of dynamically-generated toy datasets. The goal was not to make production-ready classifier, but to make model behavior visible, and allow you to play with the parameters.

https://www.danielpradilla.info/classification-visualization/compare.html

All datasets are generated synthetically in the browser. The generator creates familiar teaching shapes such as separated blobs, overlapping blobs, imbalanced classes, interlocking moons, nested rings, twin spirals, cluster islands, bridge/noisy-connector clusters, elongated clusters, outlier-heavy clusters, variable-size clusters, variable-density clusters, etc. There are controls for sample count and noise.

Charts are generated with D3