High Avoidable Bias and Data Mismatch Without High Variance
With 10% training error, 11% training-dev error, and 20% dev error, an algorithm suffers from high avoidable bias and data mismatch. It does not suffer from high variance on the training-set distribution.
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High Avoidable Bias and Data Mismatch Without High Variance
Which statement best describes how avoidable bias, variance, and data mismatch can affect a single learning algorithm?
True or False: A learning algorithm can exhibit high avoidable bias and data mismatch at the same time without necessarily having high variance.
According to Machine Learning Yearning, it is possible for an algorithm to suffer from any _____ of high avoidable bias, high variance, and data mismatch.
Which statement best describes how high avoidable bias, high variance, and data mismatch can co-exist in a single algorithm?
An algorithm can exhibit high variance and data mismatch simultaneously, without suffering from high avoidable bias.
It is possible for an algorithm to suffer from any _____ of high avoidable bias, high variance, and data mismatch.
Match each of the three error sources to the comparison that most directly reveals it.
Order the diagnostic steps for identifying which subset of the three error sources affects an algorithm.
Training error equals human-level error, training-dev error closely matches training error, but dev error is far higher. Which subset of problems is present?
An algorithm must always exhibit all three problems—high avoidable bias, high variance, and data mismatch—together; they cannot occur in isolation.
When training error ≈ human-level and training-dev ≈ training error, but dev error is much higher, the algorithm suffers from data _____ as its primary problem.
Match each two-problem combination to the diagnostic error-gap pattern it produces.
Order the reasoning steps for planning improvements when an algorithm is diagnosed with all three problems simultaneously.
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What does the 1% gap between training error (10%) and training-dev error (11%) indicate in the ML Yearning scenario?
True or False: An algorithm with 10% training error, 11% training-dev error, and 20% dev error suffers from high variance on the training set distribution.
With 10% training error, 11% training-dev error, and 20% dev error, the algorithm suffers from high avoidable bias and _____, but not high variance.
Match each error gap from the 10%/11%/20% scenario to the ML problem it diagnoses.
Order the diagnostic reasoning steps used to conclude the 10%/11%/20% algorithm has avoidable bias and data mismatch but not high variance.
Which combination of problems does an algorithm with 10% training error, 11% training-dev error, and 20% dev error have (assuming ~0% human-level error)?
True or False: In the 10%/11%/20% scenario, data mismatch contributes a larger performance drop than variance when going from training to dev error.
In the 10%/11%/20% scenario, the gap between training error and _____ error is used to assess variance on the training set distribution.
Match each ML problem type to the error evidence that confirms or rules it out in the 10%/11%/20% scenario.
Order the three error measurements from lowest to highest as reported in ML Yearning's high-avoidable-bias and data-mismatch scenario.