Classifier for large number of labels
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I have a merchants dataset with 800,000 samples and 18,000 labels. Each sample is associated with a single label and the labels are independent.
An example sample looks like
desc: int'l 0028240525 amazon uk retail amazon.co.uk => label: Amazon
In addition to the existing samples there will be new retailers added to the dataset. In this case there may well only be a single sample for that new retailer.
To summarise, I need a classifier that
- handles a large number of labels (~18,000, independent, single label per sample)
- is able to classify undersampled labels (i.e. a single retailer)
Is there an approach that will handle both? Perhaps two separate classifiers makes more sense?
machine-learning logistic-regression naive-bayes-classifier
asked 17 hours ago
Oliver Searle-BarnesOliver Searle-Barnes
1
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$begingroup$
I have a merchants dataset with 800,000 samples and 18,000 labels. Each sample is associated with a single label and the labels are independent.
An example sample looks like
desc: int'l 0028240525 amazon uk retail amazon.co.uk => label: Amazon
In addition to the existing samples there will be new retailers added to the dataset. In this case there may well only be a single sample for that new retailer.
To summarise, I need a classifier that
- handles a large number of labels (~18,000, independent, single label per sample)
- is able to classify undersampled labels (i.e. a single retailer)
Is there an approach that will handle both? Perhaps two separate classifiers makes more sense?
machine-learning logistic-regression naive-bayes-classifier
asked 17 hours ago
Oliver Searle-BarnesOliver Searle-Barnes
1
New contributor
Oliver Searle-Barnes is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I have a merchants dataset with 800,000 samples and 18,000 labels. Each sample is associated with a single label and the labels are independent.
An example sample looks like
desc: int'l 0028240525 amazon uk retail amazon.co.uk => label: Amazon
In addition to the existing samples there will be new retailers added to the dataset. In this case there may well only be a single sample for that new retailer.
To summarise, I need a classifier that
- handles a large number of labels (~18,000, independent, single label per sample)
- is able to classify undersampled labels (i.e. a single retailer)
Is there an approach that will handle both? Perhaps two separate classifiers makes more sense?
machine-learning logistic-regression naive-bayes-classifier
asked 17 hours ago
Oliver Searle-BarnesOliver Searle-Barnes
1
New contributor
Oliver Searle-Barnes is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a merchants dataset with 800,000 samples and 18,000 labels. Each sample is associated with a single label and the labels are independent.
An example sample looks like
desc: int'l 0028240525 amazon uk retail amazon.co.uk => label: Amazon
In addition to the existing samples there will be new retailers added to the dataset. In this case there may well only be a single sample for that new retailer.
To summarise, I need a classifier that
- handles a large number of labels (~18,000, independent, single label per sample)
- is able to classify undersampled labels (i.e. a single retailer)
Is there an approach that will handle both? Perhaps two separate classifiers makes more sense?
machine-learning logistic-regression naive-bayes-classifier
machine-learning logistic-regression naive-bayes-classifier
asked 17 hours ago
Oliver Searle-BarnesOliver Searle-Barnes
1
New contributor
Oliver Searle-Barnes is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 17 hours ago
Oliver Searle-BarnesOliver Searle-Barnes
1
New contributor
Oliver Searle-Barnes is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 17 hours ago
Oliver Searle-BarnesOliver Searle-Barnes
1
New contributor
Oliver Searle-Barnes is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 17 hours ago
Oliver Searle-BarnesOliver Searle-Barnes
1
asked 17 hours ago
Oliver Searle-BarnesOliver Searle-Barnes
1
1
New contributor
Oliver Searle-Barnes is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Oliver Searle-Barnes is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Oliver Searle-Barnes is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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1 Answer
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For multiclass classification problems there are multiple algorithms which are inherently built in a way to be able to solve them.
Some examples: kNN, naive bayes, decision trees...
For the performance to be accurate on all labels and for the classifier to show little bias, you can use other approaches: you can oversample minority classes or undersample majority classes, in a way that all the labels have the same number of points associated with them.
Here you can find some interesting answers about how to fight against class imbalances on decision tree classification: https://stats.stackexchange.com/questions/28029/training-a-decision-tree-against-unbalanced-data
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
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$begingroup$
For multiclass classification problems there are multiple algorithms which are inherently built in a way to be able to solve them.
Some examples: kNN, naive bayes, decision trees...
For the performance to be accurate on all labels and for the classifier to show little bias, you can use other approaches: you can oversample minority classes or undersample majority classes, in a way that all the labels have the same number of points associated with them.
Here you can find some interesting answers about how to fight against class imbalances on decision tree classification: https://stats.stackexchange.com/questions/28029/training-a-decision-tree-against-unbalanced-data
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
New contributor
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
For multiclass classification problems there are multiple algorithms which are inherently built in a way to be able to solve them.
Some examples: kNN, naive bayes, decision trees...
For the performance to be accurate on all labels and for the classifier to show little bias, you can use other approaches: you can oversample minority classes or undersample majority classes, in a way that all the labels have the same number of points associated with them.
Here you can find some interesting answers about how to fight against class imbalances on decision tree classification: https://stats.stackexchange.com/questions/28029/training-a-decision-tree-against-unbalanced-data
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
New contributor
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
For multiclass classification problems there are multiple algorithms which are inherently built in a way to be able to solve them.
Some examples: kNN, naive bayes, decision trees...
For the performance to be accurate on all labels and for the classifier to show little bias, you can use other approaches: you can oversample minority classes or undersample majority classes, in a way that all the labels have the same number of points associated with them.
Here you can find some interesting answers about how to fight against class imbalances on decision tree classification: https://stats.stackexchange.com/questions/28029/training-a-decision-tree-against-unbalanced-data
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
New contributor
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
For multiclass classification problems there are multiple algorithms which are inherently built in a way to be able to solve them.
Some examples: kNN, naive bayes, decision trees...
For the performance to be accurate on all labels and for the classifier to show little bias, you can use other approaches: you can oversample minority classes or undersample majority classes, in a way that all the labels have the same number of points associated with them.
Here you can find some interesting answers about how to fight against class imbalances on decision tree classification: https://stats.stackexchange.com/questions/28029/training-a-decision-tree-against-unbalanced-data
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
New contributor
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
New contributor
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
462
New contributor
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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Oliver Searle-Barnes is a new contributor. Be nice, and check out our Code of Conduct.
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