{"version":"1.0","type":"rich","title":"Custom Xgboost functions for Safe Aging with SPHERE Challenge","description":"This R script implements custom cross-validation and training functions for XGBoost, targeting a 20-column regression problem from the SPHERE challenge on safe aging. It trains 20 separate gradient-boosted tree models simultaneously, one per target column, using `xgb.DMatrix` and the internal `xgb.iter.update` API to update all boosters in lockstep. The cross-validation function computes a weighted Brier score across all targets, normalizes predictions per row, and supports early stopping. The code relies on the `xgboost` and `dplyr` packages, with prediction output as a data frame.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-14dc322ae72ff144a311de5a955ac6aa?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}