Random Forest combines many decision trees so that no single tree controls the result. The important learning opportunity is seeing both an individual tree and the aggregate ensemble evidence.
Open the interactive visualizer →Each tree is trained from a sampled view of the data and a subset of candidate features. Predictions are combined across trees to produce the ensemble result.
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.