Metric Optimizes the Wrong Project Objective
A metric can fail by measuring something other than what the project needs to optimize. When this happens, the metric can no longer be trusted to pick the best algorithm, and it is time to change evaluation metrics.
0
1
Tags
Machine Learning
Deep Learning
Machine Learning Strategy
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Yearning @ DeepLearning.AI
Related
Dev/Test Set Distribution Not Representative of Actual Distribution
Dev Set Overfitting from Repeated Evaluation
Metric Optimizes the Wrong Project Objective
Which scenario is a warning sign that your dev/test set or evaluation metric needs to change?
True or False: Discovering that your initial dev/test set or metric missed the mark is a serious setback that cannot be easily corrected.
If your _____ is no longer measuring what is most important to you, Ng recommends changing it rather than continuing to optimize for it.
Which of the following is the key warning sign that your dev/test set or evaluation metric may need to be changed?
Ng considers discovering that a dev/test set or metric missed the mark to be a serious setback that requires restarting the evaluation process from scratch.
If your metric is no longer measuring what is most important to your project, you should change the _____.
Match each cause of a dev set/metric incorrectly ranking classifiers to the fix Ng recommends.
Order the steps a team should take upon discovering their dev/test set or metric is no longer guiding them correctly.
Your dev set contains formal customer emails but users primarily submit short social media posts. Which cause does this best illustrate?
After changing your dev/test sets or evaluation metric, updating the project files is sufficient — there is no need to explicitly inform the team of the new direction.
If you have overfit to the dev set, Ng recommends getting more _____ data.
Match each problem scenario to the cause category it represents in Ng's framework for incorrect classifier ranking.
Order the reasoning steps for deciding whether and how to change an evaluation metric that may no longer reflect project goals.
Analyze the warning signs and causes of a development set incorrectly ranking classifiers
Diagnosing Classifier Ranking Mismatch in Spam Detection
Response to Overfitting the Development Set
Learn After
Pornographic Image Leakage as a Metric Failure Example
Changing a Metric to Penalize Unacceptable Errors
Choosing a New Trusted Metric Instead of Manual Classifier Selection
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.
Match metric issues and actions to their correct descriptions.
What is the correct sequence of decisions when a team discovers a metric alignment issue?
Analyze the consequences and resolution when a machine learning metric optimizes the wrong objective.
Diagnose a metric alignment issue in a classifier selection process.
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?