Hi David, Thanks, very good points. That is
1. C++ rather than Python (in fact this, looks like a plus for me - performance, universality, etc) 2. Complicated and inconvenient classes structure and API in Orange 3. Instability(?) I think I've heard enough good reasons to use sklearn :) Asked here because wasn't able to find such a comparison anywhere else. Denis. On 04.12.2011 17:50, David Warde-Farley wrote: > When I last tried out Orange, it was very much a C++ library trying and > failing to masquerade as a Python library. The API was complicated and > prosaic, it didn't build very easily, and it was prone to hard crashes that > brought the interpreter down in flames. I don't know if things have improved > since then (this was probably 2008ish). > > I've since moved into mostly dabbling in the kinds of algorithms that neither > scikit-learn nor Orange implement, but when I do require the use of an > off-the-shelf algorithm, I greatly prefer scikit-learn's approach to APIs > because, as a seasoned NumPy user, there's very little else I need to grasp > in order to use it. I don't need to spend half a day piecing together > somebody's notions of how best to decompose a learning task into a 30-piece > C++ class hierarchy: I look up the class I'm interested in, look at the > docstring for __init__() and fit(), and I'm done. > > David ------------------------------------------------------------------------------ All the data continuously generated in your IT infrastructure contains a definitive record of customers, application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-novd2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
