Logistic average misclassification percentage (lam%) and I-AVC(area under the ROC curve) are two important and wildly adopted measures. This paper demonstrates that a spam filter can achieve a perfect 0.00% in lam%, the minimal value in theory, by simply setting a biased threshold during the classifier modeling. At the same time, I-AVC is left untouched; and the overall classification performance reaches only a low accuracy. This means that lam% and I-AVC as main measures for spam filtering are not suitable. To solve the problem of measuring spam filtering, F-score-like measure based on ham and spam misclassification is proposed to be a single measure for spam filtering evaluation.