On 01/19/2015 10:43 AM, Timothy Vivian-Griffiths wrote: > I have used this same dataset and parameters in Rs implementation of an SVM, > and it is not outputting all 0s, so I don't think that it's a particular > problem with the data. . This seems odd. What implementation are you using in R? Scikit-learn uses libsvm, which is more or less the reference implementation for kernel SVMs. Maybe the R package you are using parametrizes the SVM in a different way.
Btw, you said: for interest the inputs matrix had shape (7763, 125) and the target vector (125,): That can not be. The input needs to be (n_samples, n_features) and the target (n_samples,) Do you only have 125 samples and 7763 features? That is very few samples for an RBF-SVM .... ------------------------------------------------------------------------------ New Year. New Location. New Benefits. New Data Center in Ashburn, VA. GigeNET is offering a free month of service with a new server in Ashburn. Choose from 2 high performing configs, both with 100TB of bandwidth. Higher redundancy.Lower latency.Increased capacity.Completely compliant. http://p.sf.net/sfu/gigenet _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
