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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