{"version":"1.0","type":"rich","title":"Some extra evaluation metrics for xgboost in R","description":"This example demonstrates two custom evaluation metrics for the xgboost package in R: MAPE (mean absolute percentage error) and RMSEE (root mean square exponential error). It defines two R functions, `MAPE` and `RMSEE`, each of which takes prediction values and a training data object, then extracts labels via `getinfo` to compute the respective error metric. The code shows how to integrate custom metrics into xgboost’s training loop by returning a list with the metric name and computed value, enabling users to monitor these alternatives during model fitting.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-eee83f333f5f3d128ab392cf0b0383ef?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}