I am working on a binary classification problem. The target variable is not linearly separable, so I've decided to use LightGBM with default parameters (I only play with n_estimators on range from 10 - 100).
When I output Gain (feature importance for LightGBM) it has extremely high values on the x-axis. When I increase the number of estimators x-axis gain grows even higher. It seems to me that the model is overfitted to a single feature .
On the other hand, split feature importances seem to have a nice distribution without extreme values on the x-axis.
What is the reason for this behavior with extreme Gain values? Regularization?