FYI it's quite common in brain imaging to use correlation of y_pred
and y_test as metric and not MSE. It's for the same reasons Stylanos
explains so I am inclined to say it could be a valuable contrib.

Alex


On Mon, Sep 7, 2015 at 10:24 PM, Stylianos Kampakis
<stylianos.kampa...@gmail.com> wrote:
> Hi Andy,
>
> It is because it loses the useful interpretation it has for linear
> regression. That is, it is no longer the variance explained by the model. It
> is rather a measure that includes the MSE scaled by the variance.
>
> There might be some contexts where this might be useful. However, on one
> hand we lose the a sense of the unit of measurement using this metric (which
> not the case for example for metrics such as MAE). On the other hand, the
> correlation between true and predicted values can be a quite simpler and
> clearer measure to use (even though this can, of course, be a matter of
> preference). The concordance correlation coefficient builds on top of that.
>
> 2015-09-07 20:38 GMT+01:00 Andy <t3k...@gmail.com>:
>>
>> On 09/07/2015 06:03 AM, Stylianos Kampakis wrote:
>> >
>> > The interpretation of R^2 is less useful for machine learning models.
>> > For example, Weka omits it all together for regression models. A
>> > useful alternative is to simply use the correlation between the true
>> > and the predicted values.
>> Can you explain this?
>> Why do you think it is not useful?
>>
>>
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