Sentence examples for misclassification which is from inspiring English sources

where the weights are defined as the percentage of RD cost increased due to misclassification, which is { W i = C i s − C i n C i n, when the CU is actually encoded in one CU W i = C i n − C i s C i s, otherwise (12).

Another potential limitation is diagnostic misclassification, which is a common problem in primary care [ 41].

This is a source of misclassification, which is likely to be random that is, not related to the exposure of interest and thus leads to underestimation of the effect estimates.

According to a simulation study conducted by de Klerk (1989), an exposure variable with an ICC of 0.42 would be associated with 32% attenuation in the estimated relative risk due to exposure misclassification, which is clearly undesirable.

This is a source of misclassification, which is likely to be random, i.e. not related to the exposure of interest, and thus lead to underestimation of the effect estimates.

The authors hypothesized that measurement error might explain their findings, suggesting that "larger aggregation improves the representativity of the exposure estimates by decreasing exposure misclassification, which is more profound when using individual stations vs. regional averages" [ 31].

This raised the reasonable hypothesis that HFH may be particularly subject to misclassification, which was differential with respect to the exposure status (in this case, represented by the maternity unit).

This is an error model from the discrete space {0,1,2} into the discrete space {0,1,2} and thus a classical misclassification problem, which is represented by the 3×3 misclassification matrix.

C-statistic traditionally assumes equal misclassification cost, which is rarely true for hospital readmission.

We calculated the total misclassification error which is the sum of the speech detection error and non-speech detection error.

However, most of the traditional loan evaluation models assume a balanced misclassification cost, which is far from business reality and leaves opportunities for investigating the cost-sensitive modelling approaches.