A Support Vector Machine separates classes by a decision surface and margin. The visual evidence becomes more useful when the margin, support vectors, kernel choice, and decision score are connected to the model state.
Open the interactive visualizer →The implemented SVM optimizes a separating decision function under the selected kernel and penalty settings. Linear and nonlinear choices change how similarity and margin constraints shape the boundary.
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.