Sentence examples for imbalanced sample from inspiring English sources

Due to the imbalanced sample size, two versions of the t test for both equal and unequal variance were utilized.

For these calculations, we randomly chose eight individuals from each population because an imbalanced sample size can bias the calculation of r.

The PowerAtlas (http://www.poweratlas.org, ref. [ 42]) allowed us to obtain an empirical estimate that the imbalanced sample sets used for these experiments with 10 hemangiosarcomas and three splenic hematomas should provide >85% power at α = 0.05 to identify true positives, although the power to identify true negatives could be lower.

As a result, an improved neural classifier is proposed, in which balanced learning with optimized decision making are introduced to enable effective learning from imbalanced samples.

These findings suggest that insufficient information on the spatial variability of soil respiration and imbalanced sampling could bias estimates of current and future carbon budgets.

In order to avoid the difficulties arising when analyzing imbalanced samples, we use the Random OverSampling Examples (Rose) approach that has been recently proposed in the statistical literature to obtain representative balanced samples from imbalanced ones.

Sampling methods for FDP are classified and reviewed by the following paired groups, namely: training sampling and testing sampling, single industry sampling and cross-industry sampling, balanced sampling and imbalanced sampling.

Focused on regression modeling between those two types of process variables with imbalanced sampling values, this paper develops a semisupervised form of the Probabilistic Partial Least Squares (PPLS) model.

We concur that the evidence for latitudinally arrayed dinosaur assemblages during the Maastrichtian is relatively weak, given the poor stratigraphic control and greatly imbalanced sampling.

To deal with the imbalanced samples, we adopt cost-sensitive ensemble technique to build the prediction models.

The sampsize parameter controls whether to use balanced or imbalanced sampling to generate the training datasets; mtry specifies the number of variables to be randomly selected and tested at each node of each tree; and ntree determines the number of trees to be grown in a forest.