Gradient Boosting builds an additive model in stages. Each stage tries to correct what the current ensemble still gets wrong, so the sequence of learners matters as much as the final result.
Open the interactive visualizer →The ensemble adds successive weak learners, scaled by a learning rate, so later stages respond to the error remaining after earlier stages.
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.