The Measure of a Model
<|reference_start|>The Measure of a Model: This paper describes measures for evaluating the three determinants of how well a probabilistic classifier performs on a given test set. These determinants are the appropriateness, for the test set, of the results of (1) feature selection, (2) formulation of the parametric form of the model, and (3) parameter estimation. These are part of any model formulation procedure, even if not broken out as separate steps, so the tradeoffs explored in this paper are relevant to a wide variety of methods. The measures are demonstrated in a large experiment, in which they are used to analyze the results of roughly 300 classifiers that perform word-sense disambiguation.<|reference_end|>
@article{bruce1996the,
    title={The Measure of a Model},
    author={Rebecca Bruce (Southern Methodist University), Janyce Wiebe (New
  Mexico State University), and Ted Pedersen (Southern Methodist University)},
    journal={In Proceedings of the Empirical Methods in Natural Language
  Processing Conference, May 1996, Philadelphia, PA},
    year={1996},
    archivePrefix={arXiv},
    eprint={cmp-lg/9604018},
    primaryClass={cmp-lg cs.CL}
}