"Good datasets for testing algorithm performance"
Dear All,
I'm creating a genetic algorithm that can do both regression and classification.
I'm comparing the algorithm against neural networks, tree learners, and nearest neighbor methods.
What are good datasets to compare on?
I'm currently browsing the UCI repository, but choices are endless.
Is there some default benchmark new algorithms are tested against?
Best regards,
Wessel
I'm creating a genetic algorithm that can do both regression and classification.
I'm comparing the algorithm against neural networks, tree learners, and nearest neighbor methods.
What are good datasets to compare on?
I'm currently browsing the UCI repository, but choices are endless.
Is there some default benchmark new algorithms are tested against?
Best regards,
Wessel
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marcin_blachnik
New Altair Community Member
I'd recomend datasets from Isabel Guyon chalanges http://clopinet.com/challenges/. These datasets are also avaliable on UCI repository. They are not to small datasets, and they are not trivial, and finally you know what are the best results - you just have to look on the competition page. Most of the other and popular datasets like Ionosphere, SpamBase, Pima Indian Diabetes, Wisconsin Brest Cancer they are all trivial, for all this methods the best results can be obtained with linear classifier. On the Duch webpage you can see the comparison of results for all popular UCI datasets :http://www.is.umk.pl/projects/datasets.html