Decision Trees build a sequence of feature rules. A useful visualizer should show the construction process and decision path rather than pretending the model trains like gradient descent.
Open the interactive visualizer →At each node the tree evaluates candidate splits using the configured criterion, selects a split, partitions samples, and continues until depth or sample constraints stop further growth.
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.