Specific type of multilabel classifcation in sklearn
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I have a model which is trained in sklearn on a 5-way classification problem, which performs relatively well (there are kNN and SVM versions, and both reproduce a test set with high accuracy).
When the model is applied in "real life", it is highly likely that many samples will contain linear combinations of multiple classes. So a sample may be 70% class A and 30% class B.
Much of what I have read about multilabel classification in sklearn relates to problems which don't fit this paradigm well, most of them are "tagging" type problems such as movie genre classification. Is there a way to apply my SVM/kNN models to this type of problem? I would prefer to only train on single-class examples but can modify the training set to create some multi-class samples too.
It seems I could work this by simply doing an indivdiual binary classifier for each class. However, this wouldn't give me the relative strength of each label, i.e. the linear coefficient. Is that possible?
classification scikit-learn multilabel-classification
asked 5 mins ago
asher1213asher1213
211
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add a comment |
$begingroup$
I have a model which is trained in sklearn on a 5-way classification problem, which performs relatively well (there are kNN and SVM versions, and both reproduce a test set with high accuracy).
When the model is applied in "real life", it is highly likely that many samples will contain linear combinations of multiple classes. So a sample may be 70% class A and 30% class B.
Much of what I have read about multilabel classification in sklearn relates to problems which don't fit this paradigm well, most of them are "tagging" type problems such as movie genre classification. Is there a way to apply my SVM/kNN models to this type of problem? I would prefer to only train on single-class examples but can modify the training set to create some multi-class samples too.
It seems I could work this by simply doing an indivdiual binary classifier for each class. However, this wouldn't give me the relative strength of each label, i.e. the linear coefficient. Is that possible?
classification scikit-learn multilabel-classification
asked 5 mins ago
asher1213asher1213
211
$endgroup$
add a comment |
$begingroup$
I have a model which is trained in sklearn on a 5-way classification problem, which performs relatively well (there are kNN and SVM versions, and both reproduce a test set with high accuracy).
When the model is applied in "real life", it is highly likely that many samples will contain linear combinations of multiple classes. So a sample may be 70% class A and 30% class B.
Much of what I have read about multilabel classification in sklearn relates to problems which don't fit this paradigm well, most of them are "tagging" type problems such as movie genre classification. Is there a way to apply my SVM/kNN models to this type of problem? I would prefer to only train on single-class examples but can modify the training set to create some multi-class samples too.
It seems I could work this by simply doing an indivdiual binary classifier for each class. However, this wouldn't give me the relative strength of each label, i.e. the linear coefficient. Is that possible?
classification scikit-learn multilabel-classification
asked 5 mins ago
asher1213asher1213
211
$endgroup$
I have a model which is trained in sklearn on a 5-way classification problem, which performs relatively well (there are kNN and SVM versions, and both reproduce a test set with high accuracy).
When the model is applied in "real life", it is highly likely that many samples will contain linear combinations of multiple classes. So a sample may be 70% class A and 30% class B.
Much of what I have read about multilabel classification in sklearn relates to problems which don't fit this paradigm well, most of them are "tagging" type problems such as movie genre classification. Is there a way to apply my SVM/kNN models to this type of problem? I would prefer to only train on single-class examples but can modify the training set to create some multi-class samples too.
It seems I could work this by simply doing an indivdiual binary classifier for each class. However, this wouldn't give me the relative strength of each label, i.e. the linear coefficient. Is that possible?
classification scikit-learn multilabel-classification
classification scikit-learn multilabel-classification
asked 5 mins ago
asher1213asher1213
211
asked 5 mins ago
asher1213asher1213
211
asked 5 mins ago
asher1213asher1213
211
asked 5 mins ago
asher1213asher1213
211
asked 5 mins ago
asher1213asher1213
211
211
add a comment |
add a comment |
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