Hi Brian, Thanks, all points are quite important for me (for most users, I think). Performance problems are surprising, considering Orange is mainly C++.
Denis. On 04.12.2011 16:57, [email protected] wrote: > Hi Denis, > > My main motivation is mostly usability. In terms of development though, > I've only really worked on decision trees, so my comments are heavily > influenced by that experience. > Here are the three main reasons why I use scikit-learn: > > Simplicity (taking the cue from Olivier). If you've seen how difficult it is > to prepare your dataset into Orange format, you will appreciate any package > that operates directly on numpy arrays. > > Speed. The decision tree implementation of Orange takes about 25 seconds to > train on the Madelon dataset, whereas the optimised version of scikit-learn > takes well under a second. I can't really comment on other algorithms though. > > Readability. Algorithms implemented in scikit-learn are meant to be easily > understood, to the point where anyone with enough knowledge of the algorithm > should be able to go in and make changes if they wish. I like to think of it > as executable pseudocode. > > These are the main reasons why I use it, but the other ones mentioned > (distributed code, licensing) are important too. > > Regards > Brian ------------------------------------------------------------------------------ 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
