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Sep 22 at 20:59 history closed Sycorax machine-learning Duplicate of What is the difference between a "link function" and a "canonical link function" for GLM
Sep 22 at 20:54 answer added Ben Bolker timeline score: 3
Sep 22 at 20:23 comment added Sycorax Some relevant threats include stats.stackexchange.com/questions/40876/… and stats.stackexchange.com/questions/20523/… and many more can be found using a search.
Sep 22 at 20:06 history migrated from math.stackexchange.com (revisions)
Sep 18 at 15:50 comment added Henry If you see linear regression as aiming at a model of $\hat y= a +bx$ ($x$ and $\hat y$ taking any real values) then you can see logistic regression as a model based on the logarithm of odds so $\log\left(\frac{\hat p}{1-\hat p}\right) = a +bx$ $\big(x$ and $\log\left(\frac{\hat p}{1-\hat p}\right)$ taking any real values, i.e. modelled probability $\hat p \in (0,1)\big)$. Logistic regression typically uses maximum likelihood estimates as least squares estimates are difficult.
Sep 18 at 15:38 comment added Will199 @AdamRubinson Done. Thank you for the suggestion.
Sep 18 at 15:24 comment added Will199 @AdamRubinson I looked at pretty much every stack exchange question related to logistic regression/function I found, including those you sent. I've also read about it on the Elements of Statistical Learning, but there they start from taking the log of the ratio of the probabilities, whose motivation I also struggled with.
Sep 18 at 15:16 comment added Will199 @AdamRubinson yes, but the course is not that much mathematical based, so we haven't seen any proofs unfortunately.
Sep 18 at 15:10 comment added Adam Rubinson Are you taking a machine learning/algorithms course? I imagine that this question is answered on some of those courses.
Sep 18 at 15:04 history asked Will199 CC BY-SA 4.0