Description: Li near r egr essi on, acti ve learning
We arriv ed at the lo gistic regression model when trying to explicitly model the uncertainty
about the lab els in a linear c la ss ifier. The same genera l modeling approach p e rmits us to
use line ar predictio ns in var ious other co ntexts as well. The simplest of them is regress ion
where the go al is to pr e dict a con tin uous resp onse y
t ∈ R to e ach example ve ctor. Here
to o fo cusing on linear predictions won’t b e inherently limiting as linear predictions can b e
easily extended (ne xt lecture).
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