The phrase "a low accuracy" is correct and usable in written English.
It can be used when discussing the precision or correctness of a measurement, prediction, or result in various contexts, such as statistics, research, or technology.
Example: "The model's performance was disappointing, as it demonstrated a low accuracy in its predictions."
Alternatives: "poor accuracy" or "limited accuracy".
All these expressions have a low accuracy.
Moreover, the SNR has a low accuracy.
In general these models are to generic and have a low accuracy.
The overall measure of accuracy in distinguishing specific congenital cardiac lesions among 13 groups of patients was 71% (range of 63% to 79%).CXR alone is not diagnostic of specific cardiac lesions, with a low accuracy of only 71%.
Bland-Altman plots revealed a low accuracy and large scatter of ADP fat mass results (mean=−2.5, 95% CI −20.3 to 15.4) compared with D2O.
The traditional thermal model of presses neglects the effect of roughness between two contact surfaces on the contact conduction coefficient, which causes the analysis to have a low accuracy.
The solver is inexact, although the error can be controlled and made as small as needed; a low accuracy solver can hence be used as an efficient preconditioner in an iterative scheme.
The existing clustering technique might cluster local frequency items at a low level, since it estimates association among items in an itemset with a low accuracy, and thus a new algorithm for clustering local frequency items is proposed.
We explore the trade-off between conservation feature representation and reserve network area, with smaller reserve networks possible if we give up on trying to meet targets for habitats mapped with a low accuracy.
It shows a low accuracy of these models and correlations in predicting the experimental data for the void fraction smaller than 0.5 while the drift flux model of Dix (Woldesemayat and Ghajar, 2007) predicts 98.3% of the entire experimental data within ±10% for the void fraction larger than 0.5.
The choice of clean training points is critical to avoid the overfitting problem which occurs generally when the model is excessively complex, which is reflected by a high accuracy over the training set and a low accuracy over the testing set (unseen points).
