When to model a problem by using the Bayes' theorem?

I have a labeled training dataset where each observation has a sentence either in English or in French as its predictors and its label (target value) is whether this sentence is English or French. The test set includes again some sentences either in English or in French but without labels.

A friend of mine suggested that we should model this problem by using the Bayes' theorem since we have have some prior values (labeled observations in training set). I agree that this can work too but I cannot really understand his argument "we should model this problem by using the Bayes' theorem since we have have some prior values".

This is because in my mind every labeled observation can be considered as a prior value and every prior value can be considered as a labeled observation so you can also apply any machine learning classification algorithm e.g. decision trees) in these cases.

Is this right in general or at least for this specific problem?

Why Bayes' theorem modeling comes up as the best solution for the problem which I described above?

Topic bayesian machine-learning

Category Data Science

1
answered at

You can model your problem with Bayes' theorem. In particular, naive Bayes classifer can be used for binary classification of text data.

Priors in a naive Bayes classifer refers to the base rate for the different classes (i.e., Are there more English or French sentences?)

Naive Bayes may or may not be the best solution. Typically, the best solution is chosen empirically by using predictive performance on a hold-out dataset.

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