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Is it possible to identify a country in a Machine Learning classification?
I have a dataset that contains numbers in different categories and is split into 50/50 train and test sets. One of the categories is country. When I get the data from the database, the country is automatically identified.
Is it possible to use that information to train a machine learning classifier?
A:
Of course it is! One very simple example would be that you could split the dataset into countries (but not 50/50), and let's assume you have around 100 records for each country. Training a naive Bayes classifier using a maximum entropy approach (e.g. scikit-learn) would have the following as training example:
Feature: Country
Label: country
Value: France
Feature: Country
Label: Spain
Value: USA
(the obvious thing to do, of course, would be to normalize the data into ranges such as 0-1, 0-100, etc.)
The test example would look like this:
Feature: Country
Label: country
Value: France
Feature: Country
Label: Spain
Value: France
Training would use the labels provided, and the classifier would predict the probability of a French person belonging to each country.
I don't know how you are using the country label in your classification, but using it in a naive Bayes classifier be359ba680
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