Improving predictive power of Binary Response model using Multi Step Logistic Approach

Published by Genpact on Nov 17, 2008

For any type of contingency or cross tabulation analysis the cells of Objective matrix can be classified into two parts - Hit and Miss. We want to maximize the Hit (# of observations falling under Diagonal) and minimize Miss (# of observations falling under Off-Diagonal or in some business case either of upper or lower triangular matrix).Relationship between this two are similar to relationship of Type I and Type II error. There are numerous ways to approach to solve/reduce this problem and come up with tolarable error. In our project we want to first predict ‘probable’ defaults (bankrupted) and then what to predict dolor associated with it. We will discuss Multi Step Logistic Model which is a Non-Optimization technique but may be one of the most useful technique when cost of Maximization/Minimization is high i.e. when proportion of 1 and 0 are distinctly different i.e. when we can only achieve goal (Tagging actual 1 as 1 and actual Zero as 0) by few % at cost of huge misclassification of other.