Learn Before
Set a New Evaluation Goal When the Current Metric Is Unreliable
If the current evaluation metric can no longer be trusted, replace it with a new metric that clearly defines the team’s objective. Do not rely for an extended period on informal manual comparison among candidate models.
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Set a New Evaluation Goal When the Current Metric Is Unreliable
What should a team do if its evaluation metric is rewarding the wrong outcome?
If an evaluation metric does not reflect the real project goal, it is still reliable for choosing the best model.
A score that measures the wrong target should not be used to ____ the best model.
Match each metric concept to its description.
What should a team do after noticing its metric points to the wrong goal?
What to do when an evaluation score favors the wrong goal
Diagnose a metric mismatch in a fraud detection model selection process.
What should a team do if its metric does not match the real objective?
What happens when an evaluation metric tracks the wrong goal?
A team should replace an evaluation metric that no longer reflects the project goal.
Learn After
What should a machine learning team do when its main evaluation score is no longer a reliable guide?
A team may continue for a long time by manually picking classifiers before defining a trusted metric.
When a Metric No Longer Reflects the Team’s Goal
What is the best response when a metric no longer matches the real objective?
Manual model picking can continue indefinitely without a trusted metric.
A better way to steer a project is to define a new _____ when the current one does not reflect the real objective.
Match each term to its role after the original project metric stops being dependable.
Put the recovery steps in order after discovering that a project metric is misleading.
Why would a team replace a vague goal with a single explicit metric?
If your evaluation metric stops being trustworthy, the best response is to pause all development until a perfect new metric is found.
Use a dependable metric instead of _____ to picking classifiers by hand.
Match each project response to what happens when a metric cannot be trusted.
Order the logic for replacing a flawed evaluation metric with a better one.
Why does choosing one evaluation metric improve model selection?
How should a team respond when its evaluation score no longer reflects real-world usefulness?
Why choose a replacement metric instead of hand-picking models?