Practical metrics for performance evaluation of estimation algorithms are discussed. A variety of metrics useful for evaluating various aspects of the performance of an estimation algorithm is introduced and justified. They can be classified in two different ways: 1) absolute error measures (without a reference), relative error measures (with a reference), or frequency counts (of some events), and 2) optimistic (i.e., how good the performance is), pessimistic (i.e., how bad the performance is), or balanced (neither optimistic nor pessimistic). Pros and cons of these metrics and the widely-used RMS error are explained. The paper advocates replacing the RMS error in many cases by a measure called average Euclidean error


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    Title :

    Evaluation of estimation algorithms part I: incomprehensive measures of performance


    Contributors:
    Rong Li, X. (author) / Zhanlue Zhao, (author)


    Publication date :

    2006-10-01


    Size :

    3626198 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English