Why is evaluation necessary after training a model?

Correct answer: To check how accurately the model works on test or new data

Model evaluation measures performance on test data or other data that were not used to fit the model. It helps estimate generalization, reveal errors, and compare suitable performance measures such as accuracy, precision, recall, or mean error. A model can perform well on training examples but poorly on new cases because of overfitting. Evaluation therefore supports a safe deployment decision. Options B, C, and D are not purposes of evaluation, so A is correct.