How to make XGBoost model to learn its mistakes
Did you verify if those samples are outliers ? If they are, try to make your model more robust to them by changing the hyperparameters or scaling your data set
My XGBoost model regularly makes mistakes in prediction on the same samples. I want to let the model know its mistakes and correct model prediction behavior. How can I do this?
I tried to solve the problem by decreasing logistic regression threshold (by increasing model sensibility) but it leads to radical increasing of false positive predictions. I also tried to tune model' parameters (colsample by tree, subsample, min_child_weight, max_depth), but it didn't help.
In text recognition software I faced some function, which allows to say the program "you predicted a letter incorrectly", and, by this, to learn model to recognize a letter correctly.
Is there something similar in Machine Learning?
May be, there are some regularization methods, which re-distribute weight between features.
Thank you.