Difference between revisions of "10-601 Logistic Regression"
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* How to implement logistic regression. | * How to implement logistic regression. | ||
* Why regularization matters. | * Why regularization matters. | ||
| − | * How logistic regression and naive Bayes are similar | + | * How logistic regression and naive Bayes are similar and different. |
| + | * What "overfitting" is, and why optimizing performance on a training set does not ''necessarily'' lead to good performance on a test set. | ||
Revision as of 10:51, 3 July 2013
This a lecture used in the Syllabus for Machine Learning 10-601
Slides
- Slides in Powerpoint. Based on the slides I used for 10-605, they might be updated.
Readings
- None
Assignment
- Implement logistic regression and apply it to a couple of datasets, using an off-the-shelf optimization routine. Experiment by changing the regularization parameter. (Details to be posted later.)
What You Should Know Afterward
- How to implement logistic regression.
- Why regularization matters.
- How logistic regression and naive Bayes are similar and different.
- What "overfitting" is, and why optimizing performance on a training set does not necessarily lead to good performance on a test set.
