Using Decision Trees in Variable Creation: Minimizing Information Loss-Part 1 - Machine Learning Times - machine learning & data science news
Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Re-examining Model Evaluation: The CRISP Approach
 The performance of prediction models can be judged using...
An Agile Approach to Data Science Product Development
 Introduction With the huge growth in machine learning over...
Wise Practitioner – Predictive Analytics Interview Series: Haig Nalbantian at Mercer – BIZ
 By: Eric Siegel, Founder, Predictive Analytics World for Business...
Is What You Did Ethical? Helping Students in Computational Disciplines to Think About Ethics
 In addition to this article, Dr. Priestly will also...
SHARE THIS:

5 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 record while

To view this content
Login OR subscribe for free

Already receive the Machine Learning Times emails?
The Machine Learning Times now requires legacy email subscribers to upgrade their subscription - one time only - in order to attain a password-protected login and gain complete access.

Click here to complete this one-time subscription upgrade

Existing Users Log In
   
New User Registration
*Required field

Comments are closed.

Pin It on Pinterest

Share This