Compared with traditional machine learning algorithm, which treats samples of different classes equally, cost-sensitive algorithm associates a cost-value with each sample to denote the different importance for identification.
If IgG titer >160 at either sampling were used to denote rickettsial exposure (preexisting or acute), 342 (38.7%) patients had exposure to SFGR and/or TGR rickettsiae.
We will use the standard hat notation to denote sample estimators of population parameters.
Here, we use the nominal gas doping ratio of [PH3]/[SiH4] to denote the samples.
In the equations that follow, we use the indices i, j, m, k and l to denote the samples, the variables, the first-level clusters, the second-level clusters and the covariate level of the sample, respectively, as where N, P, M, K and L a are their respective total numbers.
Also, we use r g, g ^, s g, g ^, s g ^ 2 and s p 2 to denote the sample correlation between the true and the predicted genetic breeding values, the sample covariance between the true and the predicted breeding values, and the sample variance of the predicted breeding value and the phenotypic sample variance, respectively.
We use X ~ N (μ, SEM) to denote a sampling distribution with mean value μ and standard error of mean SEM.
Subscripts are used throughout the text to denote the sampling site: MS – mainstem (LM – lower mainstem, UM – upper mainstem), HW – headwater and T – tributary.
We also use a superscript N to denote data from normal samples and superscript T to denote data from tumor samples.
where x denotes the sample to cluster, X i denotes the cluster i, card(X i ) denotes the number of the elements in X i, and I denotes the number of clusters.
