Hey guys, Im working on developing a web-interface, and programmatic api, to scikit-learn:
- https://github.com/jeff1evesque/machine-learning However, I've only interfaced the SVM, and SVR classes. To be thorough, for development within git, I've created unit tests for the Travis CI. But, I made up some bogus datasets, in order to unit test the SVM, and SVR predictions: - dataset: https://github.com/jeff1evesque/machine-learning/tree/master/interface/static/data - unit tests: https://github.com/jeff1evesque/machine-learning/tree/master/test/live_server But, I'd prefer to have real data, so the computed prediction is more meaningful, instead of predicating on made up data. The corresponding unit tests I have, simply check if a prediction can be made for the supplied dataset. However, I'd like to check the prediction against a known, expected result, which is the motivation of having real meaningful dataset(s): - https://github.com/jeff1evesque/machine-learning/issues/2751 Does anyone have sample dataset(s) they have used for SVM, or SVR predictions? I'd like my unit tests to be somewhat interesting, yet more meaningful. Thank you, Jeff Levesque https://github.com/jeff1evesque _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
