# mse

Computes Mean Squared Error between two vectors. It is the average of the error (squared to get rid of negative sign) made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).

## Syntax

Score = mse(targets,predictions)

## Inputs

`targets`- Actual label for each observation.
`predictions`- Predicted value for each observation.

## Outputs

- Score
- MSE of the regression model.

## Example

Usage of mse

```
targets = [3.14, 0.1, 50, -2.5];
predictions = [3.1, 0.5, 50.3, -5];
score = mse(targets, predictions);
```

```
> score
score = 1.6254
```