Hello Ted,
what are the main issues of the probability estimations of the logistic
regression ? I am developing a medical application for making
probability estimations using time series and I that's why I would know
if they are critical for me.
Many thanks and best regards,
Svetlomir.
Am
The problem is that logistic regression makes some assumptions that are
unrealistic in practice. That leads to uncalibrated probabilities in
certain cases.
This is particularly true where variable interactions are strong.
The good news is that logistic regression gives you the best answer that
I should add that the regularization will also make the logistic regression
classifier a little bit conservative about estimating probabilities near 0
or near 1.
On Fri, Jun 17, 2011 at 12:19 AM, Ted Dunning ted.dunn...@gmail.com wrote:
The problem is that logistic regression makes some
Hello Steven,
I've asked this question too:
http://mail-archives.apache.org/mod_mbox/mahout-user/201105.mbox/%3cbanlktinyohrcynt0xzrpoqqg3zkepvk...@mail.gmail.com%3E
unfortunately, Mahout's Naive Bayes implemention can't calculate
probabilities. You are now probably really astonished - I
This is what the term Naive is used in the name. The scores for this kind
of algorithm are 0 to 1 or are logarithms of such a number, but are not at
all calibrated probabilities.
And, frankly, it is rare in practice for the output of logistic regression
to be calibrated either. Those outputs
