Assuming I'm dealing with binary classification.
For what kind of data Naive bayes using expectation maximization would give a better solution and for what kind of data logistic regression would be the better choice?

  • $\begingroup$ Can you please elaborate on what is EM and what does you data looks like. $\endgroup$ May 29 at 5:05
  • $\begingroup$ @DevashishPrasad en.wikipedia.org/wiki/… I'm asking a general question about choosing the right algorithm $\endgroup$
    – Lilo
    May 29 at 9:56
  • $\begingroup$ Welcome to DataScienceSE. Normally there is no estimation of any hidden variable in binary classification, so EM is not appropriate. Maybe you have a confusion: EM is commonly used in a bayesian context but that's not the same as Naive Bayes. NB is just a simple bayesian classification algorithm. $\endgroup$
    – Erwan
    May 29 at 10:29

On a very high level -

Naive Bayes is a probabilistic model and it is scale invariant. That means scaling the data won't affect your model's performance. It is mostly used for Natural Language Processing based use cases.

Logistic Regression is a geometric model based on decision boundary. This means scaling the data will affect your model's performance. It is mostly used for traditional row column based datasets.

In Depth explanation can be found here - https://dataespresso.com/en/2017/10/24/comparison-between-naive-bayes-and-logistic-regression/


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