When a model fits the training set very closely but performs poorly on new data, that is called _____.
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Estimate the Variance from Training and Dev Errors
Training Success Does Not Guarantee Generalization
When a model fits the training set very closely but performs poorly on new data, that is called _____.
Match each concept to the description that fits a binary classifier’s error analysis.
Order the steps used to diagnose high variance in the traffic-sign classifier example.
Which training and development error pattern is most consistent with overfitting?
A model with high variance usually shows high training error and low development-set error.
Variance is computed from _____ minus training error.
Match each term to its role in diagnosing high variance in a document classifier.
Order the reasoning steps to decide whether a model is overfitting from its error numbers.
Interpreting a Classifier with Low Bias and High Variance
Estimating Bias and Variance in a Crop Disease Classifier
Term for Poor Generalization Despite Strong Training Performance