i am testing gradient boosting regressor from sklearn for time series prediction on noisy data (currency markets).
surprisingly, the the gradient boosting regressor achieves very high accuracy on the training data - surprising because the data is so noisy. however, it performs poorly on the test set.
this is clearly a case of overfitting, so i'm wondering what parameters i can change to regularize the gradient boosting regressor.
so far i've tried max_depth, reducing it to 1 (from the default of 3). this seems to work pretty well in increasing accuracy on the validation set.
does anyone know what other parameters i could tweak, to improve performance on the validation/test set? thanks