Answer: Increase the volume of data that is used in training.

## Explanation This question describes a classic machine learning problem known as **overfitting**. Here's what's happening: 1. **Overfitting Scenario**: The model performed well on the training dataset but poorly in production, which indicates the model has learned the training data too well, including noise and random fluctuations, rather than the underlying patterns. 2. **Why Option C is Correct**: Increasing the volume of training data helps the model learn more general patterns rather than memorizing specific examples. With more diverse data, the model is less likely to overfit and more likely to generalize well to new, unseen data in production. 3. **Why Other Options are Incorrect**: - **Option A (Reduce data volume)**: This would make overfitting worse by giving the model less information to learn from. - **Option B (Add hyperparameters)**: While hyperparameter tuning can help, adding more hyperparameters without proper tuning could actually increase overfitting. - **Option D (Increase training time)**: This could lead to more overfitting as the model continues to learn noise in the training data. 4. **Additional Solutions**: Other approaches to mitigate overfitting include: - Regularization techniques (L1/L2) - Dropout layers in neural networks - Early stopping during training - Cross-validation - Feature selection/reduction **Key Takeaway**: When a model performs well on training data but poorly in production, it's typically an overfitting issue, and increasing training data volume is one of the most effective ways to improve generalization.

Author: Ritesh Yadav