Sentence examples for variable classifications from inspiring English sources

At the time of its discovery, Acrocanthosaurus and most other large theropods were known from only fragmentary remains, leading to highly variable classifications for this genus.

In the case of the modified I2 grouping variable, classification was more successful in terms of correct classification percentages.

I develop a seven variable classification for political boundaries to be used with coarse-grained settlement data.

Each component yielded different conceptual meanings when variables were placed together, based on a preconceived categorical variable classification (Table  2).

Most studies were performed with expert endoscopists who used variable classification methods to differentiate adenomas from non-neoplastic polyps.

This further supports our findings which suggest that the overweight/obese variable classification may be influential in assessing the sex-specific overweight/obese and asthma association as different classifications may yield varying results.

To compare ROB for certain covariates, a reference category was chosen within each variable classification and odds ratios comparing the number of high/unclear risk trials to low risk trials were computed with 95% confidence intervals.

However, to facilitate clinical applicability the seven immune subtypes were condensed to a more simplified tumour immune subtype variable classification: high immune susceptibility, intermediate immune susceptibility and low immune susceptibility (de Kruijf et al, 2013).

This method is able to predict the quantitative variables (regression tree) and classification variables (classification tree) based on a set of qualitative and quantitative variables (Yeh 1991).

Two methods were used to model the response of a dependent variable (gullying) from a set of independent variables: Classification And Regression Trees (CART) and Multivariate Adaptive Regression Splines (MARS).

We further demonstrated that the performance of SLB forests is typically better than the performance of SLA forests with respect to the detection of discriminating variables, classification accuracy, and AUC.