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:

10 years ago
Boosting Performance of Machine Learning Models

  People often get stuck when they are asked to improve the performance of existing predictive models. What usually they do is try different algorithms and check their results. But often they end up not improving the model. Here are some of the steps you can take to boost your existing models. Add more data: More data is always useful. It helps us capture all the variance that the data has. Sometimes we may not have the option to get additional data for training. Example when you are competing in data science competitions. But while working on client

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.