Sentence examples for bias in model from inspiring English sources

On a social level, scrutiny refers to the detection of bias in model code and data.

Since the order in which variables are entered can impact their effect on the model r-square, alternating forward and backward steps reduces bias in model selection.

Best practice feature selection and an exhaustive validation procedure are followed in the presented workflow in order to ensure minimal bias in model development and evaluation.

Little apparent bias in model fit was evident against month as shown for block-level means of the two species/crosses representing the extremes in growth, E. camaldulensis x E. globulus (Figure 4a-c) and E. nitens (Figure 4d-f).

In addition, an online bias correction algorithm based on the lagged average bias was implemented to detect and correct for systematic bias in model forecasts prior to updating with the particle filter.

However, evaluation of five PTFs developed for natural soils showed that they all underestimated Ks by an order of magnitude, suggesting that application of water balance simulation models based on such PTFs to the present study site may constitute a bias in model outputs.

Buckee et al. raised this question regarding those models, further arguing that bias in models of mobility could, in turn, influence the spreading of modeled epidemics [184].

The bias in drug effect parameters such as slope, BSV on slope, and residual variance were evaluated as a mean of percent estimation error: To evaluate the predictability of QTc prolongation, bias in model-predicted ddQTc was reported as a mean of estimation error in ms at sampling time t: where i represents ith dataset and j represents jth individual.

Our study could not evaluate this due to confounding bias in models with ASB without adjustment for adiposity measures.

We develop a mathematical framework for sample selection bias in models for population structure and also proposed a correction for sample selection bias using auxiliary information about the sample.

With the presence of different perspectives and mental models, behavior elements are at the core of the problem and unintentional biases in model use may occur.