# 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?

## 3 Answers

You could try building multiple xgboost models, with some of them being limited to more recent data, then weighting those results together. Another idea would be to make a customized evaluation metric that penalizes recent points more heavily which would give them more importance.

• The OP can simply give higher sample weights to more recent observations. Most packages allow this, as does xgboost. Aug 11, 2017 at 8:55

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

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

• Thanks for your answer - its really helpful to see a coded example. How does the magnitude of the weighting function coefficients affect the model? I looked through xgboost docs, but I can't find information about the significance of these numerical values. Dec 23, 2015 at 19:29
• didn't know this trick, nice. there's a little tidbit in the xgboost doc under the function setinfo(), though its not very descriptive Dec 24, 2015 at 15:39

On Python you have a nice scikit-learn wrapper, so you can write just like this:

import xgboost as xgb
exgb_classifier = xgb.XGBClassifier()
exgb_classifier.fit(X, y, sample_weight=sample_weights_data)


More information you can receive from this: http://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBClassifier.fit

• Wish for R caret had this built in too.. Jul 3, 2017 at 13:16
• that should be xgb.XGBClassifier() in the second line of code but stackexchange does not allow edits of less than six characters... Jul 18, 2017 at 10:05