Difference between revisions of "10-601 Evaluation"
From Cohen Courses
Jump to navigationJump to search (Created page with "This a lecture used in the Syllabus for Machine Learning 10-601 === Slides === * [http://www.cs.cmu.edu/~wcohen/10-601/evaluation.pptx Slides in Powerpoint]. === Readin...") |
(→Slides) |
||
| (5 intermediate revisions by 2 users not shown) | |||
| Line 1: | Line 1: | ||
| − | This a lecture used in the [[Syllabus for Machine Learning 10-601]] | + | This a lecture used in the [[Syllabus for Machine Learning 10-601 in Fall 2014]] |
=== Slides === | === Slides === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-601/evaluation.pptx | + | * Ziv's lecture: [http://www.cs.cmu.edu/~zivbj/classF14/Model14.pdf Slides in pdf]. |
| + | * William's lecture [http://www.cs.cmu.edu/~wcohen/10-601/evaluation.pptx in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/evaluation.pdf in PDF] | ||
=== Readings === | === Readings === | ||
* Mitchell, Chapter 5. | * Mitchell, Chapter 5. | ||
| − | |||
| − | |||
| − | |||
| − | |||
=== What You Should Know Afterward === | === 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. | ||
Latest revision as of 11:55, 7 October 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
- Ziv's lecture: Slides in pdf.
- William's lecture in Powerpoint, in PDF
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.
