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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 a
> iii) What would be the best way to know whether the model is fine or
> overfitted according to your experience?
Take a look at this answer by Lars - http://stackoverflow.com/a/12254521/4016687
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Generally I do PCA and can plot the reduced dimension of the data (PC1 and
PC2). Here I'm interested in knowing the boundary decision of the
classifier.
S.
On Fri, Feb 20, 2015 at 6:34 PM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> On Fri, Feb 20, 2015 at 05:27:12PM +0100, shalu jh
On Fri, Feb 20, 2015 at 05:27:12PM +0100, shalu jhanwar wrote:
> i) Can I do it with more features (I have 16 features)?
How do you visualize a 16-features space?
G
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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
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 onl
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 m
On Fri, Feb 20, 2015 at 6:57 AM, Andy wrote:
> You give the roc_auc_score the result of "predict". You should give it
> the result of "predict_proba".
>
> This came up already quite a bit, not sure how we can avoid people making
> this mistake.
>
We can encourage people to use the scorer API mo
