Entropy Uncertainty Scorer

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.

Options

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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• 00_Guided_Labeling_for_Document_Classification_(legacy)KNIME Hub
• 01_Active_Learning_PBCA_defaultKNIME Hub
• 01_Active_Learning_PBCA_defaultKNIME Hub
• 01_Guided_Labeling_for_Document_ClassificationKNIME Hub
• 01_Guided_Labeling_for_Document_ClassificationKNIME Hub
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