Difference between revisions of "10-601 Evaluation"
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=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
| + | * The difference between sample error and true error. | ||
* What a confidence interval is. | * What a confidence interval is. | ||
* How to compute a confidence interval on the error rate of a classifier using the normal approximation. | * How to compute a confidence interval on the error rate of a classifier using the normal approximation. | ||
| − | * | + | * What a paired test is, and how to compute use a paired test to compare two classifiers. |
| + | * How to test the error rate of a classifier by cross-validation, or compare the error rates of two classifiers by cross-validation. | ||
Revision as of 14:41, 24 September 2013
This a lecture used in the Syllabus for Machine Learning 10-601
Slides
Readings
- Mitchell, Chapter 5.
What You Should Know Afterward
- The difference between sample error and true error.
- What a confidence interval is.
- How to compute a confidence interval on the error rate of a classifier using the normal approximation.
- What a paired test is, and how to compute use a paired test to compare two classifiers.
- How to test the error rate of a classifier by cross-validation, or compare the error rates of two classifiers by cross-validation.
