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I want to emphasise (increase the weight) of only a subset of data. Lets say I have old and fresh data, I would like to say that old data has to have more weight and therefore has more influence in the decision than the new data.

In scikit-learn I found only class-weight parameter, but it does not change the weight of the samples, only of all samples within the class.

Is there a way to incorporate this emphasis into the gradient boosted trees in spark or xgboost in python?

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If you have a date variable (or something similar), you can create a weight using this.

If you're using XGBoost, there is an option to specify a weight for each instance when creating the DMatrix - feed your observation weighting in here.

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There might be a fancier way to create dynamic weights but I would probably start with oversampling the subset and see how that goes. So if you've got classes A, B, and C and want to emphasize C, make a duplicate copy of C and insert that into your training data. In other words, assume you have six records to train on:

  1. A1
  2. A2
  3. B1
  4. B2
  5. C1
  6. C2

add:

  1. C1
  2. C2
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    $\begingroup$ You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change. $\endgroup$ – bradS May 17 '18 at 8:00

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