Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
The Quant’s Dilemma: Subjectivity In Predictive AI’s Value
 Originally published in Forbes This is the third of a...
To Deploy Predictive AI, You Must Navigate These Tradeoffs
 Originally published in Forbes This is the second of a...
Data Analytics in Higher Education
 Universities confront many of the same marketing challenges as...
How Generative AI Helps Predictive AI
 Originally published in Forbes, August 21, 2024 This is the...

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...