Logistic Regression, Prediction and ROC

# Pol1otape peitceil1 ceiet rbgmusml rdcrdtgm rdtts tp

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Unformatted text preview: 2/cour ses/14SS_BANA7046002/notes%284%29.html 5/15 2/17/2014 Log istic Reg r ession, Pr ediction and ROC # # Peitd rdce # Tuh # rt 0 1 # # 042 11 19 0 # # 1 21 2 4 9 There are many ways to calculate the error rate. The following is one way. The ifelse function returns a vector, the elements are 1 if actual ! = predicted, 0 otherwise. m ean gives you the percentage of 1s in the vector. ma(flecei.ri\$ ! peitdgm.nape 1 enies(rdttanY = rdce.l1isml, , 0) ) # 006 #1 .7 Out-of-sample (performance on testing set) To do out-of-sample prediction you need to add the testing set as a second argument after the glm object. Remember to add type = “response”, otherwise you will get the log odds and not the probability. po.l1otape< peitcei.l1 cei.et rbgm.usml - rdc(rdtgm, rdtts, tp ="epne) ye rsos" peitdgm.usml < po.l1otape>02 rdce.l1otape - rbgm.usml . peitdgm.usml < rdce.l1otape a.uei(rdce.l1otape snmrcpeitdgm.usml) tbecei.etY peitdgm.usml,dn= al(rdtts\$, rdce.l1otape n c"rt" "rdce") (Tuh, Peitd) # # Peitd rdce # Tuh # rt 01 # # 045 1 5 5 # # 12 82 ma(flecei.et...
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## This document was uploaded on 03/18/2014.

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