In general, the mean_squared_error
is the smaller the better.
When I am using the sklearn metrics package, it says in the document pages: http://scikit-learn.org/stable/modules/model_evaluation.html
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.
However, if I go to: http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error
It says it is the Mean squared error regression loss
, didn't say it is negated.
And if I looked at the source code and checked the example there:https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/metrics/regression.py#L183 it is doing the normal mean squared error
, i.e. the smaller the better.
So I am wondering if I missed anything about the negated part in the document. Thanks!