A model is being trained to predict housing prices based on various features like square footage, location, and number of bedrooms. The model uses the Mean Squared Error (MSE) as its loss function. Which of the following scenarios best illustrates a situation where the use of MSE would be inappropriate, and why?

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Seekh · Accepted Answer

A bad fit for MSE happens when the target values contain extreme outliers, like a dataset that has one few luxury homes priced at $10 million while most houses are around $300 k. Because MSE squares the error, that single huge mistake would dominate the loss and make the model try to predict every house near $10 million, hurting predictions for the majority. In this case the model is forced to trade off many small errors for one big one, so the loss no longer reflects the typical error we care about. Using a loss that is less sensitive to outliers, such as mean absolute error or a log‑based loss, would give a more realistic picture of performance.

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