The phrase "accuracy proportion of" is not commonly used in written English and may sound awkward.
It could be used in contexts discussing statistical analysis or measurement, particularly when referring to the ratio of accurate results to total results.
Example: "The accuracy proportion of the test results was calculated to determine the reliability of the method used."
Alternatives: "accuracy rate of" or "proportion of accuracy".
Classification accuracy (proportion of correctly classified trials) was used to evaluate classifier performance.
We reported no difference in the accuracy (proportion of correct choices) between fish trained with 8 (mean ± std. dev.: 0.753±0.556) and those trained with 4 figures as positive (0.796±0.071; independent t-test t 8) = −1.06, p = 0.319).
Performance accuracy (proportion of correct choices) was calculated over a sliding time window 30 trials wide.
The unbiased diagnostic accuracy (proportion of correct classification) was estimated to 91.24 %, against 92.65 % initially computed on the original dataset.
EPDS's accuracy (proportion of results, both positive and negative, correctly identified by the EPDS) was estimated by the area under the ROC curve.
The Penn CNB assesses both accuracy (proportion of correct responses) and speed (response time for correct responses), with the latter variable providing an additional area of exploration.
Recollection accuracy (proportions of responses attributing items to the correct source) and reaction times for the scanned retrieval task are displayed in Table 1.
Figures show row accuracy proportions for the sake of intelligibility.
As such, a majority-classifier would get a low true negative rate and thus a low balanced accuracy, whereas its conventional accuracy (overall proportion of correct classifications) can be meaninglessly high.
Using this high-risk/low-risk parameterization, a measure of the accuracy of the classification rule is evaluated, which is typically some measure of classification accuracy, the proportion of correctly classified individuals.
Classification error is 1.0 minus classification accuracy, the proportion of correct decisions, a measure we introduced in Section 8.3 (page 8.3 ).
