+1
Just wanted to point out that the K-1 subset proof is only true for binary
classification. Such heuristics do perform reasonably for the multiclass
classification criterion though.
On Monday, November 17, 2014, Alexander Hawk wrote:
> Perhaps you have become aware of this by now,
> but only
Fellow sklearners,
I am working on a classification problem with an unbalanced data set and
have been successful using SVM classifiers with the class_weight option.
I have also tried Random Forests and am getting a decent ROC performance
but I am hoping to get a performance improvement by using W
, I'll fix that)
>
> Hope this helps,
>
> Gilles
>
> On 8 February 2013 00:44, Manish Amde wrote:
>> Fellow sklearners,
>>
>> I am working on a classification problem with an unbalanced data set and
>> have been successful using SVM classifiers
Fellow sklearners,
I am working on a classification problem with an unbalanced data set and have
been successful using SVM classifiers with the class_weight option.
I have also tried Random Forests and am getting a decent ROC performance but I
am hoping to get a performance improvement by using
Using the sample_weight parameter in the RandomForestClassifier along with the
balance_weights method from the preprocessing module to generate the sample
weights might work as well.
You can check this link for a previous related discussion.
http://sourceforge.net/mailarchive/message.php?msg_id
; (This should appear in the reference, I'll fix that)
>
> Hope this helps,
>
> Gilles
>
> On 8 February 2013 00:44, Manish Amde wrote:
> > Fellow sklearners,
> >
> > I am working on a classification problem with an unbalanced data set and
> > have
Hi Sergey,
There is a sample_weights option (not very well documented) in the random
forest classifier that might help. You might want to check out the SVC example
to see the sample_weights format.
http://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html
You can provide diffe
