Machine Learning Times
EXCLUSIVE HIGHLIGHTS
The AI Paradox: More Humanlike Means Less Autonomous
  Originally published in Forbes The AI executives are at...
How To Overcome The Confidence-Killer That Destroys Most Predictive AI Projects
  Originally published in Forbes When Henry Castellanos first presented...
You Must Address These 4 Concerns To Deploy Predictive AI
 Originally published in Forbes Most predictive AI projects fail to launch into production. The...
Hybrid AI: Industry Event Signals Emerging Hot Trend
 Originally published in Forbes After decades chairing and keynoting myriad...
SHARE THIS:

12 years ago
Using Decision Trees in Variable Creation: Minimizing Information Loss-Part 1

 Numerous articles have been written about the use of decision trees to create predictive models. The literature has been rich in detail about the varying techniques and methodologies used to create decision trees. Different approaches in terms of the mathematics being used range from using Chi-Square type statistics to the more engineering-based mathematics such as the use of entropy based algorothms.   Virtually all data mining software includes some decision tree type tools. From a theoretical perspective, the more granular multivariate logistic/multiple regression techniques should outperform decision-trees since the output of the multivariate techniques is a score for each

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.