title: Is sklearn.metrics.mean_squared_error the larger the better (negated)?
body: <p>In general, the <code>mean_squared_error</code> is the smaller the better. </p>
  
  <p>When I am using the sklearn metrics package, it says in the document pages: <a href="http://scikit-learn.org/stable/modules/model_evaluation.html" rel="noreferrer">http://scikit-learn.org/stable/modules/model_evaluation.html</a></p>
  
  <blockquote>
    <p>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.</p>
  </blockquote>
  
  <p>and 
  <a href="https://i.stack.imgur.com/7XYb7.png" rel="noreferrer"><img src="https://i.stack.imgur.com/7XYb7.png" alt="enter image description here"></a></p>
  
  <p>However, if I go to: <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error" rel="noreferrer">http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error</a></p>
  
  <p>It says it is the <code>Mean squared error regression loss</code>, didn't say it is negated.</p>
  
  <p>And if I looked at the source code and checked the example there:<a href="https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/metrics/regression.py#L183" rel="noreferrer">https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/metrics/regression.py#L183</a> it is doing the normal <code>mean squared error</code>, i.e. the smaller the better.</p>
  
  <p>So I am wondering if I missed anything about the negated part in the document. Thanks!</p>