Custom Xgboost functions for Safe Aging with SPHERE Challenge

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Custom Xgboost functions for Safe Aging with SPHERE Challenge

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Last edited Aug 2, 2016

Created on Aug 2, 2016

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.

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