aritc / mse

Mean Squared Error

MSE (Mean Squared Error)

MSE measures the average amount that the model's prediction varies from the correct value. It is a measure of model's performance over a training set. The cost in Higher when the model is performing poorly on the training set.

How to run

Reads CSV file named : trainingset containing x's : input features for the model y_true : the actual value y_pred : the model's prediction and will then calculate the MSE

python ./mse/py

Explanation

For Each Training Example: We calculate the square of the difference between the predicted value and the actual value and sum them to the sum of Squared Errors and Then ultimately divde the same with ( 2 * m ) where m : No. of training Examples

We divide by m to get the average and by 2 to simply calculations going forward when we have to find the derivative of the same to minimize the cost