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). -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).