Resilience > Accuracy: Why ‘model resilience’ should be the true metric for operationalizing models

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Ingo Mierswa, Founder & CEO at RapidMiner, shared his thoughts on model resilience with UNITE.AI.

Ingo Mierswa is the Founder and CEO at RapidMiner, and recently wrote an article for UNITE.AI about the importance of model resilience. Many, if not most, use the accuracy of their models to measure model success. Ingo suggests that measuring model resilience instead gives us a much more accurate indication of model success. He notes:

“As recent world events have shown us, the world changes quickly. Now, more than ever, we need to build resilient models — not just accurate ones — to capture meaningful business impact over time. Kaggle, for example, is hosting a challenge to galvanize data scientists around the world to help build model solutions to use in the global fight against COVID-19. I anticipate that the most successful models produced as a result of this challenge will be the most resilient, not the most accurate, as we’ve seen how rapidly COVID-19 data can change in a single day.

Data science should be about finding the truth, not producing the “best” model. By holding ourselves to a higher standard of resilience over accuracy, data scientists will be able to deliver more business impact for our organizations and help to positively shape the future.”

In this article, you’ll learn about:

  • Measuring the success of machine learning
  • The difference between model accuracy and business impact
  • Drift and the importance of resilience
  • And much more

You can read the full article here: Resilience > Accuracy: Why ‘model resilience’ should be the true metric for operationalizing models