Since LinearSVC doesn't have predict_proba, one must use algorithm="SAMME",
the original AdaBoost which uses the output of "predict".
This is not exactly a linear combination because of the sign function but
still a linear SVM isn't really what I would use with Adaboost.
And it doesn't seem to improve upon a single linear SVM, see the link
below. I used SVC(kernel="linear") since it supports sample_weight.
http://mblondel.org/images/adaboost.png
M.
On Sat, Sep 27, 2014 at 3:22 PM, Andy <t3k...@gmail.com> wrote:
> On 09/27/2014 04:51 AM, Mathieu Blondel wrote:
>
>> This is because LinearSVC doesn't support sample_weight.
>>
>> I added a new issue for raising a more explicit error message:
>> https://github.com/scikit-learn/scikit-learn/issues/3711
>>
>> BTW, a linear combination of linear models is a linear model itself. So
>> you can't learn a better model than a LinearSVC() with
>> AdaBoostClassifier(svm.LinearSVC())
>>
> It is a linear combination of the "predict_probas" not the
> "decision_functions", right?
> So it is not a linear model any more (more like a neural network ;)
>
>
>
>
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