Question

Explain, with an example, when to use MSE as the loss function.

Hint:

Tip: Use MSE for regression — it penalizes large errors strongly.

Answer 1

Mean Squared Error (MSE) is one of the most commonly used loss functions for regression problems.
It measures the average of the squares of the differences between actual and predicted values.
Because it squares the errors, larger mistakes are penalized more heavily than smaller ones.
This makes MSE effective for scenarios where large errors are unacceptable.
Example: Suppose you are building a model to predict house prices based on features like area, location, and number of rooms.
Since the target value (house price) is a continuous numerical variable, MSE is appropriate to measure prediction accuracy.
The model’s goal will be to minimize the MSE, ensuring that the predicted prices are as close as possible to the actual prices.
Using MSE helps developers identify if the model needs tuning or if outliers are causing high error.
In summary, use MSE when the output is numeric and you want to minimize large deviations in predictions.