ML 4.6Lesson outlineLesson 6 of 9 in Module 4
Learning Curves and Validation Curves
Decide from two curves whether more data, more capacity or less capacity will help.
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Lesson outline: Learning Curves and Validation Curves
In this lesson9 parts
- 01What would help?
- 02Six training sizes
- 03More data always helps
- 04The learning curve
- 05How it is computed
- 06Two signatures
- 07Where curves mislead
- 08Code
- 09Recap, your challenge, and what comes next
Key terms: Learning Curves and Validation Curves
The words this lesson introduces, each in one line. The module’s glossary collects them all, and the AI glossary has every term across the courses.
- learning curve
- Training and validation score against the number of training rows.
- high-variance signature
- A training curve well above the validation curve.
- high-bias signature
- Training and validation curves that meet at a low score.
- validation curve
- Training and validation score against a hyperparameter.
Quiz: Learning Curves and Validation Curves
Your first pick on each question is the one that counts. Getting one wrong here is how the lesson sticks.