Sentence examples for model describes random from inspiring English sources
This is what happens when a statistical model describes "random noise" rather than the underlying relationship an organization is seeking to capture.
The technique is sometimes referred to as stochastic gradient boosting, as BRT includes an element of stochasticity in order to improve accuracy and reduce overfitting (when a statistical model describes random error or noise instead of the underlying relationship) [60].
This finding is reminiscent of Peterson et al. [29], who found that Maxent models performed poorly and exhibited overfitting (when a statistical model describes random error or noise instead of the underlying relationship) when used to project to unsampled regions at higher thresholds, but successfully reconstructed distributions of species at lower thresholds.
Overfitting is a problem in which a statistical model describes random error or noise instead of the underlying relationship.
A related issue is possible "overfitting" (i.e. when a statistical model describes random error or noise instead of the underlying relationship).
The smaller number of variables can also help avoid the problem of "overfitting" the model (when a model describes random noise rather than the underlying relation between variables).
This model describes the random and state-dependent arrival interval as well as service time by phase-type distribution.
In analogy with the log-normal distribution, this model describes the random variable Y= exp(X), where X is Laplace-distributed with the PDF (1), and is given by the PDF g y)=frac{alpha}{2delta} left{ begin{array}{ll} (y/delta)^{alpha-1} & text{for (0leq y < delta)}, (y/delta)^{-alpha-1} & text{for (ygeq delta)}, end{array} right.
The residual error model describes the random variability between observed and model-predicted concentration values for each patient.
The application of this model to describe random phase drifts in a neurobiological model is suggested.
Over-fitting is a particularly devastating problem, which is predictable; when there are too many parameters relative to the number of specimens or patients, the model will describe random error or noise instead of an underlying relationship.
