klaR (version 0.6-11)

ucpm: Uschi's classification performance measures

Description

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

Usage

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)

Value

• A list with elements:
• CR
  Correctness Rate
• AC
  Accuracy
• AS
  Ability to Seperate
• CF
  Confidence
• CFvec
  Confidence for each (true) class

concept

• Classification Performance Measures
• Correctness Rate
• Accuracy
• Confidence
• Ability to Seperate

Details

• Thecorrectness rateis the estimator for the correctness of a classification rule (1-error rate).
• Theaccuracyis 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, theability to seperateis based on the distances between (scaled) membership vectors and the vector representing the corresponding assigned class corner.
• Theconfidenceis the mean of the membership values of the assigned classes.

References

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

Examples

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

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