[scikit-learn] Regarding Adaboost classifier

[Jacob Schreiber]

You really need to provide more details with what exactly you're stuck
with. If you've extracted useful features from some image into a matrix X
with binary labels y you can just do `clf.fit(X, y)` to train the
classifier.

On Sat, Mar 18, 2017 at 10:21 PM, Afzal Ansari <b113053 at iiit-bh.ac.in>
wrote:

> Hello Sir,
>  I want to classify images containing negative and positive samples using
> Adaboost classifier. So how can I do that classification? Please help me
> regarding this.
>
> Thanks.
>
> On Sat, Mar 18, 2017 at 11:03 PM, Francois Dion <francois.dion at gmail.com>
> wrote:
>
>> You need to provide more details on exactly what you need. I'll take a
>> stab at it:
>>
>> Are you trying to replicate OpenCV cascade training?
>> If so, what they call DAB is Scikit learn adaboostclassifier (
>> http://scikit-learn.org/stable/modules/generated/sklearn.
>> ensemble.AdaBoostClassifier.html)‎ with algorithm=SAMME.
>> RAB is SAMME.R.
>>
>>
>> ‎Francois
>>
>>
>> Sent from my BlackBerry 10 Darkphone
>> *From: *Afzal Ansari
>> *Sent: *Saturday, March 18, 2017 00:51
>> *To: *scikit-learn at python.org
>> *Reply To: *Scikit-learn user and developer mailing list
>> *Subject: *[scikit-learn] Regarding Adaboost classifier
>>
>> Hello Developers!
>>  I am currently working on feature extraction method which is based on
>> Haar features for image classification. I am unable to find pure
>> implementation of adaboost classifier algorithm on the internet even on
>> scikit learn web. I need to train the classifier using adaboost classifier
>> to obtain Haar features from image dataset.
>> Please help me regarding this code. Reply soon.
>>
>> Thanks in advance.
>>
>>
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>>
>
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