% correct is an improper scoring rule and a discontinuous one to boot. So it
will not always agree with more proper scoring rules.
When you have a more difficult task, e.g., discriminating more categories,
indexes such as the generalized c-index that utilize all the categories will
recognize
Thats great thanks
I guess it is hard to not use % as a performance measure when that is what is
commonly used in everyday life.
So when i come to predicting the response of new data ( using the estimated
mean Y ) which i am more comfortable with i can say -
Species A - 2.12 - Therefore this
Thats great thanks,
I suppose it is hard to move away from a more traditional measure of
performance such a percentage correct, at least for the relatively amateur
statisticians among us who have been graded on such a system.
The difficulty comes in reporting the effectiveness of the model to
Thanks Frank,
I have one small question regarding this, understand you are very busy and if
you cant answer i would greatly appreciate any thoughts from the list.
Split-sample validation is not reliable unless you have say 10,000 samples to
begin with
I am a little confused. When i ran
Thanks Frank,
I have one small question regarding this, understand you are very busy and if
you cant answer i would greatly appreciate any thoughts from the list.
Split-sample validation is not reliable unless you have say 10,000 samples to
begin with
I am a little confused. When i ran the
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