You trained a regression model which has a low mean squared error on the test data, but a much higher later, when deployed by your

company. This outcome could reasonably be explained by:

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The training may not have been well regularized.

The training and test examples may have been sampled from an unrealistic data distribution.

This could be an instance of underfitting.

You may have used the test examples during training or validation.

This could be an instance of overfitting.