Optimal calibration of a ML classifier based on business knowledge
Clasificación óptima bajo decisiones de negocio Optimal calibration of a classifier based on business knowledge Versión en Español Calibrating a predictive algorithm for production in a business environment requires not only consideration of the algorithms' performance, underlying data, and related statistics, but also an economic evaluation of the related business-related actions that the algorithm will trigger. In my experience, this is a highly relevant topic but one that is not frequently considered or discussed. As a result of this, in many applications classifiers are configured without adequate consideration of business trade-offs, which is why I decided to write this post. To exemplify, consider a financial institution which is implementing a classifier (such as Logistic Regression classifier) to prevent fraudulent transactions. Of course, a fraud involves costs that the financial institution seeks to reduce. The classifier algorithm decides if each transaction that takes place in the system should be flagged as a possible fraud. Typically, such a flag triggers a series of actions that will be taken by the company, and that will also carry associated costs. What we will see next is that such costs need to be taken into account in order to adequately calibrate a predictive model. Suppose, to begin…