i) That would be quite a challenge for the human brain: In the best case you
have a hyperplane in 16 dimensions :). How can be put that into a scatter
plot!? :)
ii + iii) When I understand correctly, you want to get an idea about the
generalization error? The simplest way would maybe to look at the variance in
k-fold cross validation.
In general, I'd recommend you to read about model evaluation approaches, e.g.,
here: http://scikit-learn.org/stable/modules/model_evaluation.html
<http://scikit-learn.org/stable/modules/model_evaluation.html>
-- there is no need to look at decision boundaries...
Best,
Sebastian
> On Feb 20, 2015, at 11:27 AM, shalu jhanwar <shalu.jhanwa...@gmail.com> wrote:
>
> Hi Sebastian,
>
> Thanks a lot for your reply. Here in the examples, only 2 features are used
> to generate these plots.
>
> i) Can I do it with more features (I have 16 features)?
>
> ii) I wanna see the decision boundary of my training and testing dataset to
> see if the model is fine or it's overfitted on my data in case of both Random
> Forest and SVM.
>
> iii) What would be the best way to know whether the model is fine or
> overfitted according to your experience?
>
> Many thanks!
> Shalu
>
> On Fri, Feb 20, 2015 at 5:07 PM, Sebastian Raschka <se.rasc...@gmail.com
> <mailto:se.rasc...@gmail.com>> wrote:
> Hi, Shalu,
>
> One example for plotting decision regions would be here:
> http://scikit-learn.org/stable/auto_examples/plot_classifier_comparison.html
> <http://scikit-learn.org/stable/auto_examples/plot_classifier_comparison.html>
> It's basically a "brute force" approach: You define 2D grid of points and
> then classifier each of those points. Also, the downside is that you can only
> do that in 2D/3D.
>
> Best,
> Sebastian
>
> > On Feb 20, 2015, at 8:29 AM, shalu jhanwar <shalu.jhanwa...@gmail.com
> > <mailto:shalu.jhanwa...@gmail.com>> wrote:
> >
> > Hi guys,
> >
> > I am using SVM and Random forest classifiers from scikit learn. I wonder is
> > it possible to plot the decision boundary of the model on my own training
> > dataset so that I can have a feeling of the data? Is there any in-built
> > example available in Scikit which I can refer to view " let's say margins
> > and decision boundary" in SVM in my own data after selecting best model?
> >
> > I'd appreciate any suggestions.
> >
> > Thanks!
> > Shalu
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