Hello,
I was asking Olivier about CRF in sklearn and I ended up discussing my
experience with sklearn with him.
I am forwarding my email to this list (I hope its the right one) on his
suggestion.
Thanks to the sklearn team (especially the text classification module
authors) for helping me win a
On Thu, Sep 20, 2012 at 10:48:32PM +0200, Lars Buitinck wrote:
Below are some excerpts from the Build failed message that I got
after git rm'ing the sparse linear models code. The strange thing is
that it seems to start rebuilding in the middle of the tests.
I haven't had time to look at this,
On Fri, Sep 21, 2012 at 12:36:48PM +0200, Gael Varoquaux wrote:
I haven't had time to look at this, but the problem may lie in that the
common tests run a 'configure' step of the setup.pu, to test the
setup.py. This is probably where it fails.
OK, now that I am digging a bit more, this is not
Hello all,
Is there any method to get the separate classification accuracy score for
each class from any classifier.
The score method
SVC().fit(X, y).score(X,y)
gives accuracy of classification but not by class. I need individual score
for each class !!
Thanks
--
Sheila
I got it: there must be some files left on the server (these are files
that you just removed, right?) and coverage is trying to report some
coverage on them.
It could be .pyc, or something in the .coverage.
I seem to remember that @ogrisel already had this problem, and solved it.
Does it ring a
On 09/20/2012 09:48 PM, Lars Buitinck wrote:
Below are some excerpts from the Build failed message that I got
after git rm'ing the sparse linear models code. The strange thing is
that it seems to start rebuilding in the middle of the tests. The same
thing happened when I tried nosetests
On 09/21/2012 11:59 AM, Sheila the angel wrote:
Thanks for reply.
metrics.confusion_matrix is what I was looking for..still I need to
modify it little.
Moreover it will be great if we will have
classifier.score_by_class()
This will quite probably not happen as it blows up the API.
OR
I have a classifier which derives from RandomForestClassifier, in
order to implement a custom score method. This obviously affects
scoring results obtained with cross-validation, but I observed that it
seems to also affect the actual predictions. In other words, the same
RF classifier with two
Hi Christian,
The score method does not play any role in fit.
Are you sure the RF classifier is the same in both case? (have you set
the random state to the same value?)
Can you provide some code in any case?
Thanks,
Gilles
On 21 September 2012 20:45, Christian Jauvin cjau...@gmail.com
Hi Christian.
Why do you need to inherit from the classifier to use a different
scoring function?
That should really not be necessary.
Cheers,
Andy
--
Got visibility?
Most devs has no idea what their production app
Hi Gilles,
Are you sure the RF classifier is the same in both case? (have you set
the random state to the same value?)
You're right, I forgot about that!
I just tested it, and both classifiers indeed produce identical
predictions with the same random_state value.
Thanks,
Christian
Hi Andreas,
You mean that I could use cross_val_score's score_func argument? I
tried it once, and it didn't work for some reason, and so I sticked
with the inheritance solution, which is really a 3 line modification
anyway. Is there another way?
Best,
Christian
On 21 September 2012 15:36,
Hi Christian.
Yes, the score_func option is intended exactly for this purpose.
If you are using GridSearchCV, you have to take care of whether you have
a score or a loss function, but if you overload ``score``, you have
the same problem.
Cheers,
Andy
On 09/21/2012 08:43 PM, Christian Jauvin
Hi Andreas,
Yes, the score_func option is intended exactly for this purpose.
The problem I have with it is that my score function is defined in
terms of the probabilistic outcome of the classifier (i.e.
predict_proba) whereas the score_func's caller pass it the predicted
class (i.e. the outcome
Hello,
I am trying to classify a large document set with LinearSVC. I get good
accuracy. However I was wondering how to optimize the interface to this
classifier. For e.g.If I have an predict interface that accepts the raw
document,
and uses a precomputed classifier object, the time to
Yes, the score_func option is intended exactly for this purpose.
The problem I have with it is that my score function is defined in
terms of the probabilistic outcome of the classifier (i.e.
predict_proba) whereas the score_func's caller pass it the predicted
class (i.e. the outcome of
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