My data contains about 150 water extraction events (some features are mean flow rate, duration etc.) from toilets, showers, washing machines, taps etc. I want to use a classifier which is able to predict if an event is a toilet or not. Therefore, I have a binary classifier in mind (class 1 represents the toilet, 0 represents the rest).

I thought of logistic regression or linear discriminant analysis, but I am not sure if that would be best. In case of logistic regression I am not sure if the amount of toilets (about 25) is enough and in case of LDA although toilets observations can be considered to be drawn from a Gaussian distribution, all remaining types of fixtures are definitely not drawn from such a distribution and would be part of the 0 class of a binary classifier.

Which classification method would you take into consideration to use? Or how would evaluate which methods fits best?

  • 2
    $\begingroup$ Please note logistic is not a classifier. $\endgroup$ – SmallChess May 4 '17 at 13:28

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