AIF-C01 · Question #19
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…
The correct answer is C. Increase the volume of data that is used in training. When a model performs well on the training data but poorly in production, it is often due to overfitting. Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the…
Question
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?
Options
- AReduce the volume of data that is used in training.
- BAdd hyperparameters to the model.
- CIncrease the volume of data that is used in training.
- DIncrease the model training time.
How the community answered
(22 responses)- A5% (1)
- B14% (3)
- C73% (16)
- D9% (2)
Explanation
When a model performs well on the training data but poorly in production, it is often due to overfitting. Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the volume of data used in training can help mitigate this problem by providing a more diverse and representative dataset, which helps the model generalize better.
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