[scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

I have a project that is based on SVM algorithm implemented by libsvm . Recently I decided to try several other classification algorithm, this is where scikit-learn comes to the picture. The connection to the scikit was pretty

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

On 08/27/2016 12:33 PM, elge...@gmail.com wrote: I have a project that is based on SVM algorithm implemented by libsvm . Recently I decided to try several other classification algorithm, this is where scikit-learn comes

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

Can I update the libsvm version by myself? 2016-08-27 12:49 GMT+03:00 olologin : > On 08/27/2016 12:33 PM, elge...@gmail.com wrote: > > I have a project that is based on SVM algorithm implemented by libsvm > . Recently I decided to try > several other

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

On 08/27/2016 02:19 PM, elge...@gmail.com wrote: Can I update the libsvm version by myself? 2016-08-27 12:49 GMT+03:00 olologin >: On 08/27/2016 12:33 PM, elge...@gmail.com wrote: I have a project that is based on SVM algorith

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

So there is no possibility to reach a consistency? 2016-08-27 15:36 GMT+03:00 olologin : > On 08/27/2016 02:19 PM, elge...@gmail.com wrote: > > Can I update the libsvm version by myself? > > 2016-08-27 12:49 GMT+03:00 olologin : > >> On 08/27/2016 12:33 PM, elge...@gmail.com wrote: >> >> I have a

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

I don't think we should assume that this is the only possible reason for inconsistency. Could you give us a small snippet of data and code on which you find this inconsistency? On 27 August 2016 at 23:42, elge...@gmail.com wrote: > So there is no possibility to reach a consistency? > > 2016-08-2

Re: [scikit-learn] Fwd: inconsistency between libsvm and scikit-learn.svc results

One logical possibility is if svm would accept the scikit-learn changes. On 8/27/16 6:42 AM, elge...@gmail.com wrote: So there is no possibility to reach a consistency? 2016-08-27 15:36 GMT+03:00 olologin >: On 08/27/2016 02:19 PM, elge...@gmail.com

Re: [scikit-learn] Latent Semantic Analysis (LSA) and TrucatedSVD

I am not sure this is exactly the same because we do not center the data in the TruncatedSVD case (as opposed to the real PCA case where whitening is the same as calling StandardScaler). Having an option to normalize the transformed data by sigma seems like a good idea but we should probably not c

Re: [scikit-learn] Latent Semantic Analysis (LSA) and TrucatedSVD

BTW Roman, the examples in your gist would make a great non-regression test for this new feature. Please feel free to submit a PR. -- Olivier ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] GradientBoostingRegressor, question about initialisation with MeanEstimator

This comes from Algorithm 1, line 1, in "Greedy Function Approximation: a Gradient Boosting Machine" by J. Friedman. Intuitively, this has the same effect as fitting a bias (intercept) term in a linear model. This allows the subsequent iterations (decision trees) to work with centered targets. Ma