Classifier with 14% Estimated Bias and 16% Estimated Variance
A classifier with estimated bias of 14% retains substantial training error relative to the selected lowest-plausible-error benchmark. Its estimated variance of 16% indicates a further substantial increase in error on held-out data. The classifier therefore shows both poor training fit and poor generalization, so describing it only as underfitting or only as overfitting captures only part of its error pattern.
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
High Bias and Data Mismatch with a Small Training Gap
How can a single learning algorithm show avoidable bias, variance, and data mismatch at the same time?
True or False: A learning system can have both high avoidable bias and a data mismatch problem even if its variance is not high.
An algorithm can have any _____ of high avoidable bias, high variance, and data mismatch.
How can underfitting, overfitting, and distribution shift appear in the same model?
High Variance and Domain Mismatch Can Occur Together Without High Bias
A model may contain any _____ of three common error sources.
Match each error source to the comparison that best exposes it.
Order the checks used to diagnose bias, variance, and distribution shift.
A model has about the same error as expert performance on the training set, its training-dev error is nearly identical to its training error, and its dev-set error is much worse. What issue is most likely present?
If a classifier has high avoidable bias, it must also have high variance and a data mismatch problem.
A Large Training-Dev Gap Indicates Data Mismatch
Connect each error pattern to the combination of issues it signals.
Plan the Next Fix When Three Error Sources Are Present
Why Multiple Error Sources Can Appear at the Same Time
Diagnosing Multiple Error Sources in a Voice Transcription Model
Can Different Error Problems Appear Separately?
Classifier with 14% Estimated Bias and 16% Estimated Variance
Learn After
A classifier shows about 15% bias and 15% variance. Which description fits best?
True or False: When a classifier shows both high bias and high variance, the usual overfitting/underfitting labels do not cleanly describe the situation.
A classifier with high bias usually performs _____ on the training data.
Identify the Bias and Variance Pattern
Put the diagnostic steps in order for deciding whether a model has high bias, high variance, or both.
In the worked bias–variance diagnosis, what were the two estimated error rates?
True or False: A model that has both high bias and high variance can be overfitting and underfitting at the same time.
Equal Bias and Variance in a Fraud Detector
Match each model-performance pattern to the most likely diagnosis.
Reasoning About Bias and Variance From Training and Validation Results
Explain why a model can show both underfitting and overfitting at the same time.
Diagnosing a Fraud Model with 12% Training Error and 27% Dev Error
What happens when a model has both high bias and high variance?