Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
How Generative AI Helps Predictive AI
 Originally published in Forbes, August 21, 2024 This is the...
4 Ways Machine Learning Can Perpetuate Injustice and What to Do About It
 Originally published in Built In, July 12, 2024 When ML...
The Great AI Myth: These 3 Misconceptions Fuel It
 Originally published in Forbes, July 29, 2024 The hottest thing...
Where FICO Gets Its Data for Screening Two-Thirds of All Card Transactions
 Originally published in The European Business Review, March 21,...

CRISP-DM

Three Critical Definitions You Need Before Building Your First Predictive Model

 Portions excerpted from Chapter 2 of his book Applied Predictive Analytics (Wiley 2014, http://amzn.com/1118727967) Successful predictive modeling is more than identifying the right algorithms. And, even though 60-90% of our time is spend on data preparation before deploying the first predictive model built from a new data set, successful predictive modeling goes well beyond effective

Employee Churn 202: Good and Bad Churn

 Our prior article on this venue began outlining the business value for solving “the other churn” – employee attrition. We introduced the “quantitative scissors” with a simple model of employee costs, benefit, and breakeven points. The goal...

Big Data Continued…

Big Data is not a singular concept but rather a label for a range of data issues. A few months ago I wrote an article about the Volume, Velocity, and Variety (and other “V’s”) of big...

The Role of Analysts After Model Deployment

Last month I made the case for discussing model deployment. One of the mistakes I see organizations make related to deployment is this: after the model is deployed, there is little or no thought about that model...

Why Don’t We Talk about Deployment?

The Cross Industry Standard Process for Data Mining (CRISP-DM) is the leading published methodology for Data Mining (DM), and by extension, Predictive Analytics (PA). I use it routinely as I lead PA projects and when I...