Logistic Regression, Prediction and ROC

Html 415 2172014 log istic reg r ession pr ediction

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Unformatted text preview: ning ob. hs(rdc(rdtgm,tp =&quot;epne) itpeitcei.l1 ye rsos&quot;) 3.Last but not least, you want a binary classification decision rule. The default rule is if the fitted then . The value 0.5 is called cut-off probability. You can choose the cut-off probability based on mis-classification rate, cost function, etc. In this case, the cost function can indicate the trade off between the risk of giving loan to someone who cannot pay (predict 0, truth 1), and risk of rejecting someone who qualifys (predict 1, truth 0). https://blackboar d.uc.edu/bbcswebdav/pid- 9566224- dt- content- r id- 55868231_2/cour ses/14SS_BANA7046002/notes%284%29.html 4/15 2/17/2014 Log istic Reg r ession, Pr ediction and ROC These tables illustrate the impact of choosing different cut-off probability. Choosing a large cut-off probability will result in few cases being predicted as 1, and chossing a small cut-off probability will result in many cases being predicted as 1. tbepeitcei.l1 tp =&quot;epne)&gt;05 al(rdc(rdtgm, ye...
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