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.

- Column Selection
- Include the columns containing the class probabilities. The values must sum up to 1 for each data row.
- Output column name
- Set the name of the appended output column.
- Invalid Input Handling
- Specify the action if a data row of the input is invalid.
Invalid
could mean a missing value in
the input or
an
invalid distribution
(the
probabilities
must sum up to 1). If
*Fail*is selected, the node will fail. Otherwise, the node just gives a warning and puts missing values in the output for the corresponding rows.

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