What should the company do to mitigate this problem?

A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model’s performance decreased significantly.

What should the company do to mitigate this problem?
A . Reduce the volume of data that is used in training.
B . Add hyperparameters to the model.
C . Increase the volume of data that is used in training.
D . Increase the model training time.

View Answer

Answer: C

Explanation:

The issue described is likely caused by overfitting, where the model performs well on the training dataset but fails to generalize to unseen data. Increasing the volume of training data can help mitigate overfitting by providing the model with more diverse examples, improving its ability to generalize to new data in production.