If the performance of a classification model on the test set (out-of-sample) error is poor, you can just re-calibrate your model parameters to achieve a better model.

Yes

No

Suppose you derived a classification model. The error you obtained on the training set is low and the error on the test set is large. The model suffers from...

under-fitting the data

over-fitting the data

If your model under-fit the data (recall the general statement describing trees: Top is green AND Bottom is brown), introducing more features to make the model more complex will help.

True

False