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.

Diagnose the 10%/11%/20% error scenario

Analyzing Error Metrics for Data Mismatch

Identify the missing error problem