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Unformatted text preview: .high (scalar) the high end of the rule range .nanis (scalar) how NaNs are handled: NaN, 0 or 1 best (vector) indeces of rules which make the best combined rule sig (vector) significance measure values for each rule, and for the combined rule Cm (matrix) A matrix of vectorized confusion matrices for each rule, and for the combined rule: [a, c, b, d] (see below). For each rule, such rules sR.low <= x < sR.high are found which optimize the given significance measure. The confusion matrix below between the given grouping (G: group - not G: contrast group) and rule (R: true or false) is used to determine the significance values: G not G --------------- accuracy = (a+d) / (a+b+c+d) true | a | b | |-------------- mutuconf = a*a / ((a+b)(a+c)) false | c | d | --------------- accuracyI = a / (a+b+c) See also SOM_DREVAL, SOM_DRTABLE. [ SOM Toolbox online doc ]...
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This note was uploaded on 05/23/2010 for the course CS 245 taught by Professor Dunno during the Spring '10 term at Aarhus Universitet.
- Spring '10
- Machine Learning