Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
AI and ML in Health Care: A Brief Review
 Of the many disciplines that are active users of...
Visualizing Decision Trees with Pybaobabdt
 Originally published in Towards Data Science, Dec 14, 2021....
Correspondence Analysis: From Raw Data to Visualizing Relationships
 Isn’t it satisfying to find a tool that makes...
Podcast: Four Things the Machine Learning Industry Must Learn from Self-Driving Cars
    Welcome to the next episode of The Machine...
SHARE THIS:

2 years ago
Measuring Invisible Treatment Effects with Uplift Analysis

  Models make predictions by identifying consistent correlations in what has been observed, but we usually require more than predictions to know what action we should take. For example, knowing that older people are more likely to have heart disease is a good first step, but knowing behaviors or treatments that will reduce the risk of heart disease as we age is actionable. Knowing millennials are more likely to buy your product than gen Z is nice, but knowing which marketing approach will persuade gen Z to buy is valuable. In this election season, knowing who will vote

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.