Interpreting Training and Dev Error When Both Fit and Stability Are Poor
A learning curve can show both high bias and high variance when the training error is far above the target level and the development error is much higher than the training error. In that situation, the model needs changes that address both underfitting and instability.
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Reading a High-Bias Learning Curve When the Train-Dev Gap Is Small
High Variance Clue from Learning Curves
Interpreting Training and Dev Error When Both Fit and Stability Are Poor
Why plot training error and development error on the same graph?
True or False: Looking only at the error from the largest training sample gives the full story of how performance changes as data increases.
Generalization Curves
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How to build and read a learning curve for a model
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Choosing a plot to judge whether more labeled data is worth the effort.
Why plot training and validation error together?
What does the far-right point on a learning curve indicate?
One data point is enough to understand how a model will change as more training examples are added.
Learn After
Recognizing When Both Bias and Variance Are High
If a model has both high bias and high variance, its training error and development error are usually about the same.
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Reading Training and Validation Patterns
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What a learning curve suggests when both underfitting and overfitting are present
Evaluating a Sales Forecast Model
What does a large validation gap indicate when training error is already high?
What should you do when the learning curve suggests both weak fit and high instability?
When a classifier has high bias, it usually achieves very low training error.