ucpm function

Uschi's classification performance measures

Function to calculate the Correctness Rate, the Accuracy, the Ability to Seperate and the Confidence of a classification rule.

ucpm(m, tc, ec = NULL)

Arguments

• m: matrix of (scaled) membership values
• tc: vector of true classes
• ec: vector of estimated classes (only required if scaled membership values are used)

Details

• The correctness rate is the estimator for the correctness of a classification rule (1-error rate).
• The accuracy is based on the euclidean distances between (scaled) membership vectors and the vectors representing the true class corner. These distances are standardized so that a measure of 1 is achieved if all vectors lie in the correct corners and 0 if they all lie in the center.
• Analougously, the ability to seperate is based on the distances between (scaled) membership vectors and the vector representing the corresponding assigned class corner.
• The confidence is the mean of the membership values of the assigned classes.

Returns

A list with elements: - CR: Correctness Rate

• AC: Accuracy

• AS: Ability to Seperate

• CF: Confidence

• CFvec: Confidence for each (true) class

References

Garczarek, Ursula Maria (2002): Classification rules in standardized partition spaces. Dissertation, University of Dortmund. URL http://hdl.handle.net/2003/2789

Author(s)

Karsten Luebke, karsten.luebke@fom.de

Examples

library(MASS)
data(iris)
ucpm(predict(lda(Species ~ ., data = iris))$posterior, iris$Species)