Machine Learning Times
EXCLUSIVE HIGHLIGHTS
AGI Is Infeasible. Instead, Pursue Superhuman Adaptable Intelligence
  Originally published in Forbes On a recent episode of the...
Artifact-Driven Development: Making It Possible to Query Large Analytics and AI Projects
 A practical introduction to making complex project structure explicit...
Incoherent AGI Hype Spurs An Industrywide Pivot To Hybrid AI
  Originally published in Forbes Recently on The Dr. Data Show,...
The AI Paradox: More Humanlike Means Less Autonomous
  Originally published in Forbes The AI executives are at...
SHARE THIS:

6 years ago
Overcoming the Explainability Challenges of Machine Learning Models

 Some History Machine Learning Models, which have historically been referred to as predictive models, are not new. Any early practitioner in this field would emphasize that the two key deliverables of any model are as follows: its benefits to the business or organization Model Explainability (i.e. what is inside the model) The model benefits are essentially about optimizing ROI where the challenge might be to identify those key metrics that impact ROI.  For a marketing campaign, the use of the model helps the marketer to better allocate his or her budget towards those individuals who are more likely

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.