The search for great machine learning models is about overcoming conflicts. We want accurate models, but we don’t want them to overfit. We also want more features to improve accuracy, but not too many to avoid the curse of dimensionality. So simultaneously optimizing multiple conflicting criteria seems like it should be a standard solution in the data science toolbox.

Join RapidMiner Founder and President, Dr. Ingo Mierswa for this webinar where he discusses:

  • Multi-objective optimization: the secret to great modeling
  • Methods for applying it in machine learning and feature engineering
  • How to apply these methods in RapidMiner