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Hello, I am using Sklearn in combination with Pytables for Automated Genre Identification of Web Pages. The reason I am using Pytables is for executing Very hight Scale Evaluation of SVM using 50,000 samples for training. I know that that might probably this will not have so much impact in my results. However, due to the extreme chaotic nature of the textual information on the WEB, I would like to assure that the Scale will have not Impact on learning or vice versa. So, is there a any tip for me to fit() the model in stages i.e not to bring the whole data set in Memory during the learning process. As I can see in my code when I am giving an EArray as an argument to Fit() it seem to load everything in memory in order to train the model, so I cannot exploit the Pytables feature i.e Arrays to "Live" on the Disk and not on the Ram. Any Enlightenment tips for me? Thank you very much. Best Regards, Dimitrios ------------------------------------------------------------------------------ Try before you buy = See our experts in action! The most comprehensive online learning library for Microsoft developers is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, Metro Style Apps, more. Free future releases when you subscribe now! http://p.sf.net/sfu/learndevnow-dev2 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Try before you buy = See our experts in action! The most comprehensive online learning library for Microsoft developers is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, Metro Style Apps, more. Free future releases when you subscribe now! http://p.sf.net/sfu/learndevnow-dev2 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
