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Can a metric that measures the wrong objective still be trusted to select the best machine learning algorithm?
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Machine Learning Strategy
Supervised Learning
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What action should a team take when their evaluation metric no longer aligns with the project's actual objectives?
Can a metric that measures the wrong objective still be trusted to select the best machine learning algorithm?
If a metric optimizes the wrong objective, you can no longer trust it to _____ the best algorithm.
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What should a team do when their metric cannot be trusted to choose the best model?
Why does measuring the wrong objective invalidate an evaluation metric?
Should a team change their metric if it fails to guide them to the best algorithm?