Hi Daniel.Could you please open an issue and maybe provide a sample script that demonstrates that
changing the sample weights doesn't change the decision function?
That sounds like a bug. Cheers, Andy On 07/21/2013 02:08 PM, Daniel Vainsencher wrote:
I encountered similar problems. Weighting libsvm inputs under sklearn seems unsafe at any speed... Amusingly enough, I think the pretty demo at [1] demonstrates the problem. Notice how the white samples are really dominant... but the decision boundary is consistent with uniform weights. If you change the script slightly, its obvious changing the weight ratio does not change the displayed decision boundary, so it is not a problem of merely scaling or interaction with C. This may be related to the fact that sample weighting in libsvm itself does not appear to be a stable core feature; [2] implies that a special version of libsvm is needed. Or is that already taken into account? [1]http://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html [2]http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances <http://www.csie.ntu.edu.tw/%7Ecjlin/libsvmtools/#weights_for_data_instances> -- Daniel Vainsencher ------------------------------------------------------------------------------ See everything from the browser to the database with AppDynamics Get end-to-end visibility with application monitoring from AppDynamics Isolate bottlenecks and diagnose root cause in seconds. Start your free trial of AppDynamics Pro today! http://pubads.g.doubleclick.net/gampad/clk?id=48808831&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
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