Extra Trees is a tree ensemble that adds extra randomness to how candidate splits are formed. Comparing it with a single Decision Tree or Random Forest makes the effect of ensemble diversity easier to inspect.
Open the interactive visualizer →Multiple randomized trees are constructed and their outputs are aggregated. Depth, estimator count, feature selection, and sampling settings determine the ensemble's capacity and diversity.
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.