Sheiner L B, Beal S L
J Pharmacokinet Biopharm. 1981 Aug;9(4):503-12. doi: 10.1007/BF01060893.
The performance of a prediction or measurement model is often evaluated by computing the correlation coefficient and/or the regression of predictions on true (reference) values. These provide, however, only a poor description of predictive performance. The mean square prediction error (precision) and the mean prediction error (bias) provide better descriptions of predictive performance. These quantities are easily computed, and can be used to compare prediction methods to absolute standards or to one another. The measures, however, are unreliable when the reference method is imprecise. The use of these measures is discussed and illustrated.
预测或测量模型的性能通常通过计算相关系数和/或预测值与真实（参考）值的回归来评估。然而，这些方法对预测性能的描述并不充分。均方预测误差（精度）和平均预测误差（偏差）能更好地描述预测性能。这些量很容易计算，可用于将预测方法与绝对标准或相互之间进行比较。然而，当参考方法不精确时，这些测量是不可靠的。本文对这些测量方法的使用进行了讨论和说明。
