Why is negative (MSE or MAS) Scoring parameter like- neg_mean_absolute_error in SKLEARN is considered for regression model evaluation
It's simple: minimizing MSE is equivalent to maximizing negative-MSE.
An objective function that the scorer can maximize is just by "convention" as the Sklearn documentation suggests.
I an a novice in Machine Learning and while going through the course I came across the "Scoring Parameter". I understood for Regression model evaluation, we consider the negatives of Mean Squared error, mean absolute error etc.
When I wanted to know the reason, I went through SKLearn documentation which says "All scorer objects follow the convention that higher return values are better than lower return values. Thus metrics which measure the distance between the model and the data, like metrics.mean_squared_error, are available as neg_mean_squared_error which return the negated value of the metric."
This explanation does not answer my why's completely and I am confused. So, why is the negatives taken more because logically if the difference in prediction is higher whether -ve or +ve, it makes our models equally bad. Then why is it that scoring parameter is focused on negative differences?