Logistic Regression, Prediction and ROC

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Unformatted text preview: _, aiy ioil cei.ri) rdttan ACcei.l0 I(rdtgm) # 11 #1 73 ACcei.l1 I(rdtgm) # 19 #1 81 BCcei.l0 I(rdtgm) # 21 #1 10 BCcei.l1 I(rdtgm) # 11 #1 96 Understanding classification decision making using logistic regression To get prediction from a logistic regression model, there are several steps you need to understand. Refer to textbook/slides for detailed math. 1.The fitted model gives you the estimated value before the inverse of link (logit in case of logistic regression). In logistic regression the are called log odds ratio, which is . In R you use the predict() function to get a vector of all in-sample (for each training ob). hs(rdc(rdtgm) itpeitcei.l1) https://blackboar d.uc.edu/bbcswebdav/pid- 9566224- dt- content- r id- 55868231_2/cour ses/14SS_BANA7046002/notes%284%29.html 3/15 2/17/2014 Log istic Reg r ession, Pr ediction and ROC 2.For each , in order to get the P(y=1), we can apply the inverse of the link function (logit here) to . The equation is . In R you use the fitted() function or predict(,type=“response”) to get the **predicted probability* for each trai...
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This document was uploaded on 03/18/2014.

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