Hi Stelios.
I haven't heard of that, but I'm no expert on the subject. How does that
compare to Cohen's kappa, which I think is the standard for two rater
agreement?
Can you give references of its use and why it is chosen?
And why would you use it as a metric for a regression model instead of R^2?
Why do you say it is robust?
Best,
Andy
On 09/04/2015 08:15 AM, Stylianos Kampakis wrote:
Hello everyone,
I was thinking to add the concordance correlation coefficient as a
metric for regression models and I wanted to ask first whether you
think this is a good idea.
The concordance correlation coefficient is a measure of inter-rater
agreement. I stumbled upon it about a year ago, and I realized it is a
really useful for evaluating regression models.
In short, the RMSE gets too affected by outliers. The correlation can
be a good alternative in this case. However, the correlation ignores
systematic bias in the predictions. So, it can still be misleading in
some cases. The concordance correlation coefficient measures how
closely the predicted and the true values fall on the 45 degree line
through the origin.
https://en.wikipedia.org/wiki/Concordance_correlation_coefficient
Regards,
Stelios
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