A company built a model to predict item prices. The model performed well on the training data but its performance dropped significantly after deployment. What should the company do to address this issue?

The model's strong performance on training data but poor performance in deployment indicates overfitting. Overfitting occurs when a model learns the training data too well, including noise and specific patterns, making it generalize poorly to new, unseen data. Increasing the volume of data used for training helps mitigate overfitting by providing the model with a broader and more representative sample of the underlying data distribution. This encourages the model to learn more generalizable patterns rather than memorizing the training set. Reducing data volume would worsen overfitting. Adding hyperparameters can increase model complexity and potentially exacerbate overfitting if not carefully tuned. Increasing training time without more data might further entrench overfitting.