Calculates the entropy uncertainty score of a class probability distribution. Input are rows containing class probabilities P = p1, p2, ..., pn that must sum up to 1. Output will be the normalized Shannon entropy . This is defined by E(P) = H(P) / log(n) with H(P) = - sum(p_i*log(p_i) for each i in 1,...,n. The logarithm with base 2 is used. The normalization leads always to values between 0 and 1. A uniform probability distribution (i.e., most uncertain as all probabilities are equal to each other) has an entropy value of 1. If one of the class probabilities is 1 and the others 0, the highest certainty is given and the entropy value will be 0.
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