@Surya1987 wrote:
Initially I built a model for Loan Prediction like below:
model1<-glm(Loan_Status~Gender+Married+Dependents+Education+Self_Employed+Property_Area+ApplicantIncome+CoapplicantIncome+LoanAmount+Loan_Amount_Term+Credit_History,family=binomial(link='logit'),data=n)Area Under the Curve: 0.8081062
Significant at 0.05% Level: Married, Property_Area and Credit History
So I modified the model by keeping only the significant predictors
model2<-glm(Loan_Status~Married+Property_Area+Credit_History,family=binomial(link='logit'),data=n)
Area Under the Curve: 0.7969799
So is it right to say that model1 is better as AUC is comparatively more or Model2 is better as I am predicting with fewer variables which is giving AUC slightly lower than Model1.
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