The phrase "an error of the model" is correct and usable in written English.
It can be used when discussing mistakes or inaccuracies that arise from a particular model, often in contexts like data analysis, machine learning, or statistical modeling.
Example: "The results were skewed due to an error of the model, leading to incorrect conclusions."
Alternatives: "a flaw in the model" or "a mistake in the model".
We assume that those discrepancies arise from an error of the CPCM model in estimating the hydration energy for metal cations.
When the LY30 and TRAIL effects were combined, our inability to simulate the observed dynamics of caspase-8 suggested an error in the model.
Combined modeling of the diseases led to a loss in predictive performance (increased predictive error) of the model, with an approximate six-fold increase in cv deviance above the average cv deviance for all four diseases analysed individually (Table 2).
Minifying intervals in data transformation leads to an extreme reduction in the error of the model.
The penalized error represents a balance between the squared error of the model and the number of covariates in the model.
It was observed that the model for the CO consumption shows an error of ±19 % while the models for the other hydrocarbons show a maximum error of ±21%%.
Aggregate difference is the prediction error of the models using an independent sample of trees (e.g. testing sample; trees not included in the sample used to fit the models).
These validation procedures tend to indicate that the 2nd-round models can also be used to predict the responses with an inherent deviation quantified by the analytical error of the models.
In general, the predictions agreed with the measured results within an error of 25%, and the new models can be used for the design of natural ventilation systems.
The prediction errors of the model (a model with subtracting minimum + 2nd derivative pre-processing) for ethanol contents were 0.1% (w/w).
However, here the ANOVA does not satisfy the requirement for normality of the residual errors of the model, using a Jarque-Bera test (P<0.01) [33].
