On 09/08/2015 06:15 PM, Andreas Mueller wrote:
I'm also +1 on Pearson, and unsure about concordance ;)
On 09/08/2015 10:43 AM, Mathieu Blondel wrote:
Maybe I misunderstood Alex's comment but I thought he meant (Pearson)
correlation.
Alex, when you mentioned brain imaging, did you mean Pearson
correlation or concordance correlation?
I can't comment on concordance correlation as I was not familiar with it.
On Tue, Sep 8, 2015 at 3:12 PM, Andreas Mueller <t3k...@gmail.com
<mailto:t3k...@gmail.com>> wrote:
On 09/08/2015 06:42 AM, Mathieu Blondel wrote:
Pearson correlation between y_true and y_pred is also a
standard evaluation metric in genomic selection. In a sense,
it can be seen as a ranking measure since y_true and y_pred
don't need to be equal: they only need to be collinear to
achieve perfect correlation.
+1 for adding pearson_correlation_score
I thought we were discussiong the concordance correlation
coefficient?
Little off-topic thoughts :)
Maybe it's time to create something like additional package for
scikit-learn, to add every cutting-edge algs and methods there?
Something like Boost for C++ STL? If someone wants to implement
something - let he implement it and add new method to this package,
users will use it and decide if it was usable for them. If a method
really good and somehow useful at practice you can always move it to
main scikit-learn package.
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