Mean Squared Error (MSE)

There are three error metrics that are commonly used for evaluating the performance of a regression model; they are:

• Mean Squared Error (MSE).
• Root Mean Squared Error (RMSE).
• Mean Absolute Error (MAE)

In this article I m sharing about the Mean Squared Error :-.

Mean squared error (MSE) measures the amount of error in statistical models. It assesses the average squared difference between the observed and?predicted values in the dataset.

Where:

• yi?is the ?observed value.
• ?i?is the ?predicted value.
• n = the number of observations.

When a model has no error, the MSE equals zero. As model error increases, its value increases.

If all the observed value fall very close to the best fit line in linear regression model, then in that case , the MSE is minimum. If the observed value are apart from the line then we can say that the value of MSE is large.

Effect of squaring :

1. Squaring the formula helps in transform the negative value into positive .
2. MSE is not robust to outliers. The squaring inflating or magnifying large errors. If the larger the difference between the predicted and observed values, the larger the resulting squared positive error. This has the effect of penalize models more for larger errors when MSE is used as a loss function.

Root Mean Squared Error

The?Root Mean Squared Error, or RMSE, is an extension of the mean squared error. The square root of the MSE is calculated