<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Logit on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/logit/</link><description>Recent content in Logit on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Fri, 10 Jul 2026 13:18:55 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/logit/index.xml" rel="self" type="application/rss+xml"/><item><title>Logistic Regression</title><link>https://abaj.ai/wiki/ml/supervised/regression/logistic/</link><pubDate>Fri, 10 Jul 2026 13:02:51 +1000</pubDate><guid>https://abaj.ai/wiki/ml/supervised/regression/logistic/</guid><description>&lt;p>logistic regression is the method that seems only ever to be used for &lt;em>classification&lt;/em> yet insists on calling itself regression. the resolution: it &lt;strong>is&lt;/strong> regression — of the log-odds of a bernoulli success probability onto a linear predictor.&lt;span class="margin-note" data-note="this page follows my math5806 (applied regression analysis) week 4 notes, which follow dobson and barnett">
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this page develops it the honest way, as a generalised linear model: bernoulli response, canonical logit link, likelihood fitted by fisher scoring, inference through the deviance. the machine-learning reading (cross-entropy loss, linear decision boundaries) falls out at the end as a corollary.&lt;/p></description></item></channel></rss>