Hi folks, The problem is that when I set fobj to my customized objective function, the prediction error I receive after training differs from what is reported by feval at the last iteration. The errors are identical, as expected, if I re...
Description I first train an lgb.Booster with a custom objective function. I then try to update the leaf values using another dataset, but it fails on an assertion that the objective must not be None. Reproducible example import lightgbm...
28,29,30], changing the training objective has not been thoroughly investigated thus far. This study directly addresses this gap by investigating the effectiveness of a variety of recently published imbalance-insensitive loss functions for training Gradient Boosting classifiers. In this work, ...
I want to use a custom loss function for LGBMRegressor but I cant find any documentation on it. If I understand it correctly I need to use the params 'objective' and 'metric' to completely change the loss function in training and evaluat...
525 524 params['objective'] = 'none' 526 525 for alias in _ConfigAliases.get("num_iterations"): 527 526 if alias in params: 528 - warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias)) 527 + _log_warning("Found `{}` in params. Will use ...
Fit an Primary model to quickly analyse the variables and discard variables that don't contribute at least min_gain_percent_for_pruning percent reduction to the training's objective. We recommend that this model be fit with the hyperparameter feature_fraction = 1.0 (the default) so that each ...