The Conference for Machine Learning Innovation

Evaluating machine learning solutions for subjective problems

Session
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Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Infos

Machine Learning research uses massive curated datasets and mathematical metrics to evaluate their methods, which allows models to be compared up to decimal points of percentages. While these metrics are not always perfect, they mostly provide a good indication of how well the model performs. When deploying machine learning systems in the real world, we often encounter problems where it’s harder to evaluate the quality of the results. 

This is especially the case when dealing with solutions that can only be evaluated subjectively, where the model decision influences the outcome, or where the possible result set is so large that it’s impossible to create a golden evaluation set. Prime examples of this are search engines and recommender systems, where all three of these characteristics come into play together, but there’s a wide range of machine learning solutions that are hard to evaluate due to at least one of these characteristics. 

The only way to really evaluate these systems is by running them in production and analyzing the results, which can be risky, costly, and too late. In this talk, we’ll examine some alternative and intermediate evaluation methods, which can help you build and evaluate these systems with more confidence and lower risk.

This Session originates from the archive of Diese Session stammt aus dem Archiv von BerlinBerlin . Take me to the program of . Hier geht es zum aktuellen Programm von Singapore Singapore .

This Session originates from the archive of Diese Session stammt aus dem Archiv von BerlinBerlin . Take me to the program of . Hier geht es zum aktuellen Programm von Berlin Berlin .

This Session originates from the archive of Diese Session stammt aus dem Archiv von BerlinBerlin . Take me to the program of . Hier geht es zum aktuellen Programm von Munich Munich .

This Session Diese Session originates from the archive of stammt aus dem Archiv von BerlinBerlin . Take me to the current program of . Hier geht es zum aktuellen Programm von Singapore Singapore , Berlin Berlin or oder Munich Munich .

Behind the Tracks