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High Bias and High Variance Bias-Variance Example
A classifier with estimated bias of 15% and variance of 15% has both high bias and high variance: it performs poorly on the training set and performs even worse on the dev set. In this case, overfitting and underfitting terminology is hard to apply because both are happening.
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Related
Bias (Informal Definition)
Variance (Informal Definition)
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Bias-Variance Tradeoff
Learning Curve for Dev-Set Error
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Low Bias and Low Variance Bias-Variance Example
According to Machine Learning Yearning, what are the two major sources of error in machine learning?
Understanding bias and variance helps you decide whether adding more training data or other tactics to improve performance are a good use of time.
According to Machine Learning Yearning, the two major sources of error in machine learning are bias and _____.
Which two fundamental error components does Andrew Ng identify as targets for ML optimization?
Understanding bias and variance helps you decide whether adding more training data is a good use of time.
Machine Learning Yearning identifies _____ and variance as the two major sources of error in machine learning.
Match each term to its role in ML Yearning's two-major-sources-of-error framework.
Order the conceptual steps a practitioner follows when applying the bias-variance framework to guide improvement efforts.
What practical benefit does ML Yearning say comes from understanding bias and variance?
Machine Learning Yearning describes bias and variance as the only sources of error in machine learning.
Understanding bias and variance helps you decide whether _____ are a good use of time.
Match each child concept to the aspect of the bias-variance framework it addresses.
Order the reasoning steps a practitioner takes when deciding whether adding training data will improve performance.
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Learn After
Which statement best describes a classifier with an estimated 15% bias and 15% variance?
True or False: The overfitting/underfitting terminology applies clearly when a classifier has both high bias and high variance.
A classifier with high bias performs _____ on the training set.
Match each term to its defining characteristic in Andrew Ng's high bias/high variance scenario.
Order the steps to determine whether a classifier has high bias, high variance, or both.
In Andrew Ng's high bias/high variance example in Machine Learning Yearning, what are the two estimated error values?
True or False: A classifier with both high bias and high variance is simultaneously overfitting and underfitting.
In Andrew Ng's example, both the estimated bias and estimated variance are _____.
Match each classifier performance pattern to its error diagnosis.
Order the reasoning steps Andrew Ng uses to conclude a classifier has both high bias and high variance.