Where can I find datasets usefull for testing my own Machine Learning implementations? I am currently trying to implement some Machine Learning algorithms on my own. Many of them have the nasty property of being hard to debug, some bugs don't cause the program to crash, but rather work not as intended and seem as the algorithms just gives weaker results.
I would like to have some way of increasing my confidence in the implementation, for example if I had some small datasets, with additional information "Algorithms X worked for Y iterations and had results Z on this dataset", that would be really helpful. Has anyone heard of such datasets?
 A: The UCI repository mentioned by Bashar is probably the largest, nevertheless I wanted to add a couple of smaller collections I came across:


*

*Datasets from the Mulan Java library

*Datasets from the Auton lab of Carnegie Mellon University's School of Computer Science

*Datasets used in the Book Elements of Statistical Learning

*Several datasets from KDD Cup competitions

*Datasets at the Department of Statistics, University of Munich

A: From the UC Irvine Machine Learning Repository:

We currently maintain 223 data sets as a service to the machine learning community. You may view all data sets through our searchable interface. Our old web site is still available, for those who prefer the old format. ... If you wish to donate a data set, please consult our donation policy. ... We have also set up a mirror site for the Repository.

Also, the following MIAS dataset has been widely used and studied:

When benchmarking an algorithm it is recommendable to use a standard test database (data set) for researchers to be able to directly compare the results. Most of the mammographic databases are not publicly available. The most easily accessed databases and therefore the most commonly used databases are the Mammographic Image Analysis Society (MIAS) database and the Digital Database for Screening Mammography (DDSM). Besides, there are currently few projects developing new mammographic image databases as well as several old projects.

