Machine Learning Times
EXCLUSIVE HIGHLIGHTS
2 More Ways To Hybridize Predictive AI And Generative AI
  Originally published in Forbes Predictive AI and generative AI...
How To Overcome Predictive AI’s Everyday Failure
  Originally published in Forbes Executives know the importance of predictive...
Our Last Hope Before The AI Bubble Detonates: Taming LLMs
  Originally published in Forbes To know that we’re in...
The Agentic AI Hype Cycle Is Out Of Control — Yet Widely Normalized
  Originally published in Forbes I recently wrote about how...
SHARE THIS:

10 years ago
Faster Credit Scoring Dev With Specialized Binning Code – R Package

 

Introduction

One of the main concerns in a credit scoring project is the extraordinary amount of time required for its development, usually months after the data has been collected.

Most of this time is concentrated on a particular stage of the development called the “Generation of Predictive Characteristics”, a process that generates a list of features that have the potential to become part of the final model.

This list can be huge depending on the creativity of the modeling team, likely on the hundreds mark, and the analysis of each one of them is the reason why it is so time consuming.

This article discusses the usage of specialized code to dramatically decrease the time spent on generating predictive characteristics using a real life example from a Chilean Bank while developing one of its credit scoring models for account management.

This content is restricted to site members. If you are an existing user, please log in on the right (desktop) or below (mobile). If not, register today and gain free access to original content and industry news. See the details here.

Comments are closed.