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

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`m` |
matrix of (scaled) membership values |

`tc` |
vector of true classes |

`ec` |
vector of estimated classes (only required if scaled membership values are used) |

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.

A list with elements:

`CR` |
Correctness Rate |

`AC` |
Accuracy |

`AS` |
Ability to Seperate |

`CF` |
Confidence |

`CFvec` |
Confidence for each (true) class |

Karsten Luebke, karsten.luebke@fom.de

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

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