Hi, From liblinear documentation: Usage: train [options] training_set_file [model_file]
Where options is as follows: -s type (loss/penalty function) -c cost -p epsilon -e epsilon -B bias -wi weight -v n-fold cross validation -q I understand how to relate –s, -c, -wi, -v, -q. If fit_intercept is true, as it is by default, _get_bias returns intercept_scaling (which is 1 by default), so I get the –B bias for liblinear will be 1 and not -1? Regarding –p and –e, I’m not sure which values sklearn is passing to liblinear. Probably one of the two is tolerance but I’m not sure. Does anyone know? Thank you, From: Manoj Kumar [mailto:[email protected]] Sent: Friday, August 08, 2014 11:12 AM To: [email protected] Subject: Re: [Scikit-learn-general] mapping liblinear wrapper with LinearSVC Hi, I think you need to be looking at the recent master of scikit-learn LinearSVC inherits from BaseLibLinear, the call to train_wrap is made in this line https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/svm/base.py#L719 . You can see the parameter (method) self._get_bias() , which corresponds to https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/svm/base.py#L783 . This is set to -1.0 if intercept is not fit and intercept_scaling if fit_intercept is True. I am not really sure about the epsilon parameter though (I will have to look in detail) , however the default tol is 1e-4. HTH --- Regards, Manoj Kumar, GSoC 2014, Scikit-learn Mech Undergrad http://manojbits.wordpress.com
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