Comparison of Sensitivity for Consumer Loan Data Using Gaussian Naïve Bayes (GNB) and Logistic Regression (LR)
Rahul Pundlik · 2016
If the training sample size is asymptotic (number approaching to infinity) classification accuracy for Logistic Regression is often better than the asymptotic accuracy of GNB. Also if the training sample size is scarce, classification accuracy for GNB is often better than the asymptotic accuracy of Logistic Regression. This article shows that, although the Bayesian classifier's probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero-one loss (misclassification rate) even when this assumption is violated by a wide margin. We have attempted to validate the above two distinct features on the consumer loan data with a sample size of 1000 and 500,000.