The phrase "accuracy of the rules" is correct and usable in written English.
It can be used when discussing the precision or correctness of specific guidelines or regulations.
Example: "The accuracy of the rules is essential to ensure fair play in the competition."
Alternatives: "correctness of the rules" or "precision of the rules".
In association rule example, 90% shows confidence or accuracy of the rule.
Accuracy of the rule base depends on the distribution of data points in the data sheet over the range of application.
To assess the predictive accuracy of the rule, we constructed a 2×2 table for calculation of the following: sensitivity, specificity and positive (PPV) and negative predictive values (NPV).
However, the accuracy of the rule may be improved by using a different cut point to identify 'at risk' patients; therefore future research should examine the clinical value of the rule at different cut points.
The accuracy of the rule was confirmed using 1,000 bootstrap resamples For each and every measure of predictive validity, the estimate obtained with the bootstrap was very close to the original estimate (Table 4).> -wrap-foot> *95% asymptotic confidence intervals.
The overall accuracy of these rules was compared using the mean and standard deviation of 100 AUC values per model by using the corrected, resampled t-test [ 36].
The accuracy of these rules can be assessed with percentages of correct classification or odds-ratios that are easily understood.
In overall, it is aimed to show that finite element analysis not only can assess the accuracy of the design rules specified in current codes of practice but also can improve and propose more accurate design rules.
Approximately 75% of the total segments were used to define rule sets for each LC class (see Table 1) and 25% to assess the accuracy of the defined rules over the entire classified image.
