I would say that being scale-invariant and having a fixed range is one
of the main benefits of using R^2.
On 09/07/2015 04:24 PM, Stylianos Kampakis 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
<mailto: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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