What is the purpose of the evaluation phase in the machine learning model lifecycle?
To assess the model’s performance and accuracy
Explanation:
The evaluation phase in the machine learning lifecycle is used to measure how well a trained model performs using evaluation metrics such as accuracy, precision, recall, and F1-score. It helps determine whether the model is reliable and ready for deployment.
Why not the others?
- a) To collect more data for training → This is part of data preparation or improvement steps.
- b) To deploy the model into production → This happens after successful evaluation.
- d) To choose the best algorithm → Algorithm selection usually happens earlier during model development.
