Use the previously trained model for further prediction in catboost

Question

I want to find optimal parameters for doing classification using Catboost. I have training data and test data. I want to run the algorithm for say 500 iterations and then make predictions on test data. Next, I want to repeat this for 600 iterations and then 700 iterations and so on. I don't want to start from iteration 0 again. So, is there any way I can do this in Catboost algorithm?

Any help is highly appreciated!

Answer 1

You can run the algorithm for the maximum number of iterations and then use CatBoost.predict() with ntree_limit parameter or CatBoost.staged_predict() to try different number of iterations.