It looks like you should use the
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.html
to convert y_train into a binary indicator matrix format that scikit-learn
can work with.
On 25 March 2016 at 18:42, Enise Basaran <basaranen...@gmail.com> wrote:
> Hi,
>
> I'm studying on web page classification and I have 32 categories like
> 'Adult', 'Business&Economy', 'Education', etc.
>
> OneVsRestClassifier example is below :
>
> X_train = np.array(["new york is a hell of a town",
> "new york was originally dutch",
> "the big apple is great",
> "new york is also called the big apple",
> "nyc is nice",
> "people abbreviate new york city as nyc",
> "the capital of great britain is london",
> "london is in the uk",
> "london is in england",
> "london is in great britain",
> "it rains a lot in london",
> "london hosts the british museum",
> "new york is great and so is london",
> "i like london better than new york"])
> y_train = [[0],[0],[0],[0],[0],[0],[1],[1],[1],[1],[1],[1],*[**0,1],[0,1**]*]
>
> But I don't want to label data as above [0,1], because as you know *it's very
> difficult to find multilabelled data*. So that I generated 32 binary dataset
> for 32 category. When a test content came for prediction, test content is
> being sent to all classifiers and I'm taking into account only classifiers
> that are returning 'Yes'. So I could make multilabelled classification with
> my own dataset.
>
> I can evaluate precision, recall and f-measure values for each classifier(for
> each category) but how can I test my all dataset(all classifiers) ? Thanks
> for your help in advance.
>
>
>
> On Thu, Mar 24, 2016 at 10:26 PM, Joel Nothman <joel.noth...@gmail.com>
> wrote:
>
>> OneVsRestClassifier already implements Binary Relevance. What is unclear
>> about our documentation on model evaluation and metrics?
>>
>> On 25 March 2016 at 00:13, Enise Basaran <basaranen...@gmail.com> wrote:
>>
>>> Hi everyone,
>>>
>>> I want to learn binary classifier evaluation metrics please. I
>>> implemented "Binary Relevance" method for multilabel classification.
>>> *[1] * My classifiers say "Yes" or "No". How can I calculate accuracy
>>> score of my dataset, what metrics can I use for my binary classifiers?
>>> Thanks in advance.
>>>
>>>
>>> *[1] Binary Relevance (BR)* is one of the most popular approaches as a
>>> trans-formation method that actually creates k datasets (k = |L|, total
>>> number of classes), each for one
>>> class label and trains a classifier on each of these datasets. Each of
>>> these datasets contains the same number of instances as the original data,
>>> but each dataset D λ j , 1 ≤ j ≤ k positively labels instances that belong
>>> to class λ j and negative otherwise.
>>>
>>> Sincerely,
>>>
>>>
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>>>
>>
>>
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>
>
> --
> *Enise Başaran*
> *Software Developer*
>
>
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> Intel Data Analytics Acceleration Library.
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>
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