Using Human Performance to Estimate the Best Possible Error
For a wildlife-audio classifier, expert listeners can identify most clear recordings with almost no mistakes, so the best achievable error for the task is close to 0%. By contrast, if 12% of river-monitoring clips are so distorted by wind and machinery that even specialists cannot tell what is present, then an excellent system would still be expected to make about 12% error on that portion of the data.
0
1
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
Using Human Performance to Estimate the Best Possible Error
What does comparing human labels with the training set estimate?
Human Labels Can Help Estimate the Best Achievable Error Rate on Easy Human Tasks
Estimating the Best Human-Level Error on an Easy Task
Match each term to the description that fits a human-label-based estimate of the best possible error rate.
Arrange the steps for estimating the best achievable error rate from human labels.
Which pair of tasks is most suitable for using human labels to estimate a practical lower bound on error?
Human Label Accuracy Is Checked Against the Training Set
Human-Friendly Labeling Tasks
Match each step in the human-label estimate procedure to what it is for.
Order the logic for using human labels as a proxy for the best achievable error rate.
Using Human Labels to Estimate Best-Possible Error
Estimating an Error Floor for Bird Photo Classification
Human-label tasks for estimating the best possible error
Learn After
Use the Strongest Practical Human Benchmark as a Proxy
Estimating Human Error on Mobile Cat Images
A Large Training-to-Validation Gap Signals High Variance
Why is the optimal error rate for detecting a bicycle in a clear photo close to 0%?
If 8% of satellite images are impossible for experts to label correctly because thick cloud cover hides the target, the best achievable classification error is approximately 8%.
Human-level performance is often used as a proxy for the _____ error rate on a task.
Match each situation with the approximate best possible error rate suggested by human performance.
Order the steps for using human performance as a stand-in for the best achievable error rate and deciding whether to reduce bias.
A defect detector has 12% error, while expert annotators have 4% error. What is the avoidable bias and what should you do next?
Assuming a Zero Error Floor
In a task where people almost never miss the correct answer, the ideal error rate is nearly _____.
Match each term to its meaning when human performance is used as a practical benchmark for model error.
Order the steps for deciding whether a task has a near-zero or a much higher best-possible error rate.
How model and expert error comparison reveals bias
Selecting a Desired Error Rate Proxy for a Noisy Dictation System
What information should a person use when checking one stage of a pipeline against human performance?