Part 3 Comparing the Information Gain of Alternative Data and Models.txt

Comparing the Information Gain of Eggertopia Scores and Your Model Both the Eggertopia Scores and your binary classification model can be thought of as tools to reduce uncertainty about future default outcomes of credit card applicants. Your own model, deeloped in !art ", identifies dependencies between, on the one hand, the si# types on input data collected by the ban$, and on the other hand, the binary outcome default%no default. If we assume that the dependencies identified by Eggertopia Scores and by your model on the &est Set are stable and representatie of all future data 'a big assumption( we can draw some further conclusions about how much information gain, or reduction in uncertainty, is proided by each. )efinitions are gien in the Information Gain Calculator Spreadsheet, proided below. Information Gain Calculator.#ls# *uestion+ *uestion+ n your model� s &est Set results, results, what is the condition conditional al entropy of default, gien your test classificationsint+ you need need your model� s true positie rate from from !art !art ", *uestion *uestion "/, and � test incidence incidence� 0proportion 0proportion of eents your your model classifies classifies as default1 default1 from from !art ", 2uestion "3. "3. 4se the condition condition incidence incidence of /56 and your your model� s &rue !ositie !ositie rate to calculate the portion of &!s. &hen you hae the inputs needed to use the Information Gain Calculator Spreadsheet. "

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7ecall that the entropy of the original base rate, minus the conditional entropy of default gien your test classification, e2uals the Mutual Information between default and the test. I'89Y( : '8(

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&he population of potential credit card customers consists of /56 future defaulters. &he base rate incidence of default './5, .