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I have a classification problem. I am trying to classify strings using

I normalize the data before training.

So far I get the best results with addition, but I cannot understand why. The difference is quite significant (2 percentage points in f-measure and accuracy).

for example:

P(abc) = p(a) * p(b) * p(c)

or

P(abc) = p(a) + p(b) + p(c)

or

P(abc) = log(p(a)) + log(p(b)) + log(p(c))

In all of the examples, P / len(abc).

Multiplication seems to be the theoretically correct way to go, but the results are contradicting.

Can anyone venture an explanation? thanks in advance

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    $\begingroup$ The question needs WAY more explanation of what you are trying to do. So far all I can tell you is that if you multiply numbers that are smaller than 1, then the result keeps getting smaller. If you add the same numbers, the sum obviously grows bigger. $\endgroup$ – LauriK Jan 21 '15 at 14:49

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