xgboost: give more importance to recent samples Is there a way to add more importance to points which are more recent when analyzing data with xgboost?
Just add weights based on your time labels to your xgb.DMatrix.
The following example is written in R but the same principle applies to xgboost on Python or Julia.

data &lt;- data.frame(feature = rep(5, 5),
                   year = seq(2011, 2015), 
                   target = c(1, 0, 1, 0, 0))
weightsData &lt;- 1 + (data$year - max(data$year)) * 5 * 0.01

#Now create the xgboost matrix with your data and weights
xgbMatrix &lt;- xgb.DMatrix(as.matrix(data$feature), 
                         label = data$target, 
                         weight = weightsData)