Vs (MSE & RMSE)

Description

MSE (Mean Squared Error) and RMSE (Root Mean Squared Error) are both used to measure the accuracy of a model's predictions, with a focus on regression tasks.

• MSE quantifies the average squared difference between predicted and actual values. The larger the MSE, the worse the model's performance.
• RMSE is simply the square root of MSE, bringing the error back to the same unit scale as the original data, making it more interpretable.

Differences

• MSE emphasizes larger errors more due to the squaring of the differences. It can be sensitive to outliers.
• RMSE provides a more tangible error metric as it's in the same units as the target variable.

Use Cases

• Use MSE when you want to penalize larger errors more heavily.
• Use RMSE when you want a metric that is in the same units as the target for easier interpretation.