xgb.train() method takes a
learning_rates parameter, which can take a custom function to apply a dynamic learning rate, depending on the current training round.
I recently posted a paper explaining how I'm using it to both speed up training in the beginning, and making more precise towards the end.
However, there's a problem with this method: it tends to overfit on the
eval set because there's no cross-validation.
I noticed that
xgb.cv() method has no
learning_rates parameter and therefore appears to not allow for a dynamic learning rate while doing CV.
So my questions:
- Is there a possibility to use dynamic learning rates with
- If not, is there a rationale behind this or is this just not implemented yet?