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>
wrote:
> Hi, Shalu,
>
> One example for plotting decision regions would be here:
> 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>
> 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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