I need to improve the prediction result of an algorithm that is already programmed based on logistic regression ( for binary classification).
I tried to use XGBoost and CatBoost (with default parameters). but it takes a long time to train the model (LR takes about 1min and boost takes about 20 min). and if I want to apply tuning parameters it could take more time for fitting parameters.
I want to ask if there are any suggestions to apply fastly boosting methods. And if there are other ways to get better performance I hope to mention them, please.
Ps: My data is about 280 000 simples and 247 (numerical) features;