Skip to content
Interactive visualization of the most important parameters in gradient boosting
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.


This interactive application illustrates the mechanics of the most important parameters in tree-based gradient boosting. You can try out the app online. For lower latency, clone the repository, open server.R in RStudio, and click on Run App.

Input parameters

You can modify the most important parameters in XGBOOST models

  • Gamma
  • Minimum child weight
  • Sample size
  • Maximum depth
  • Maximum number of boosting iterations
  • Early stopping
  • Learning rate

Partial dependency plot

The partial dependency plot shows how well the learned function approximates the underlying data generating process. This is particularly interesting as you change the parameters Gamma, Minimum child weight, as well as the Sample size of the training data.

Screenshot - partial dependency

Loss function

This tab plots the performance (RMSE) on the training set as well as the test set against the number of boosting iterations. This is particularly interesting as you change the Learning rate, Max. number of boosting iterations, and Early stopping.

Screenshot - loss function

Check out the online version:

You can’t perform that action at this time.