Logistic Regression, Prediction and ROC

# L1isml rbgmnape peitdgmnape rdcel1isml aueirdcel1isml

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Unformatted text preview: rsos&quot; .) # # # FLE # AS # # 40 50 tbepeitcei.l1 tp =&quot;epne)&gt;02 al(rdc(rdtgm, ye rsos&quot; .) # # # FLE TU # AS RE # # 47 30 10 3 tbepeitcei.l1 tp =&quot;epne)&gt;1-4 al(rdc(rdtgm, ye rsos&quot; e0) # # # TU # RE # 40 # 50 In-sample and out-of-sample prediction In-sample (performance on training set) Suppose the cut-off probability is choosen as 0.2. The 2nd statement generates a logical vector (TRUE or FALSE) of whether each ob in training set has a fitted probability greater than 0.2. The 3rd statement transforms the logical vector to numeric (0 or 1). po.l1isml &lt; peitcei.l1 tp = rbgm.nape - rdc(rdtgm, ye &quot;epne) rsos&quot; peitdgm.nape&lt; po.l1isml &gt;02 rdce.l1isml - rbgm.nape . peitdgm.nape&lt; rdce.l1isml a.uei(rdce.l1isml) snmrcpeitdgm.nape Next we look at the confusion matrix, dnn is used to label the column and row: tbecei.ri\$,peitdgm.nape dn= al(rdttanY rdce.l1isml, n c&quot;rt&quot; &quot;rdce&quot;) (Tuh, Peitd) https://blackboar d.uc.edu/bbcswebdav/pid- 9566224- dt- content- r id- 55868231_...
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## This document was uploaded on 03/18/2014.

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