ArioBattolla

I solved this issue by transforming it into dataframe object: ``` lb = aml.leaderboard.as_data_frame() print(lb.head()) ```

I did: ``` sm = models[model](**par) sm.set_training_values(X_train, y_train) sm.train() with open("best_models.pickle", "wb") as handle: pickle.dump(sm, handle) ``` I got this error: **PicklingError:** Can't pickle : attribute lookup function on builtins...

Ah ok, thank you very much. What can I do for other models, such as RBF or IDW?

It gives me this error: > File "W:/17_Users/trainees/Battolla/manda\regression.py", line 647, in AutoInterpolation > pickle.dump(best_models, handle) > > File "stringsource", line 2, in smt.surrogate_models.rbfclib.PyRBF.__reduce_cython__ > > TypeError: no default __reduce__ due...

But if I save KPLS, do I need to retrain the model or can I load directly to predict new points? Because I am not able to do that.