In the context of Sequence Transduction Models, which of the following statements best illustrates the concept of overfitting in their application to various business domains?

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Seekh · Accepted Answer

Overfitting in sequence transduction models happens when the model learns the exact patterns of the training data instead of the underlying business rule, so it predicts training sequences perfectly but fails on new ones. For instance, a model trained to generate personalized email sequences might produce the exact same responses for every customer it has seen before, but when a new customer with a different buying pattern enters, the model gives irrelevant suggestions. The key idea is that the model’s parameters have been tuned too closely to the training examples, losing the ability to generalize. A concrete example is a churn prediction system that memorizes the last few months of customer interactions and predicts churn only for those exact patterns, missing new churn signals in fresh data. Thus, overfitting shows up as high training accuracy but low real‑world performance.

In the context of Sequence Transduction Models, which of the following statements best illustrates the concept of overfitting in their application to various business domains?

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