The phrase "able to correctly classify" is correct and usable in written English.
It can be used when discussing the capability of a system, model, or individual to accurately categorize or identify items or information.
Example: "The new algorithm is able to correctly classify images of animals with high accuracy."
Alternatives: "capable of accurately categorizing" or "competent in proper classification".
The model was able to correctly classify 81% of the classified wells in cross-validation tests for intermediate casing.
Indeed, when large numbers of carriers are retained, seven of the nine SVM classifiers presented in the table are able to correctly classify over 95% of the test set examples.
Results have shown that the classifier built from a smooth specimen was able to correctly classify other specimens with the same and with a different material layup, and is capable of recognizing signals from notched specimens, thus providing interesting and encouraging indications in view of the application on real structures.
We additionally showed that a classifier based on gene expression variability is able to correctly classify the patients of an independent validation dataset into the two different disease subtypes, confirming the importance of expression variability in the study of CLL.
A multi-class approach will only be able to correctly classify those botnets for which it has been trained.
The discriminant analysis was able to correctly classify 92.20% of the individual students into their appropriate SES group.
We were able to correctly classify 21 (60%) of the 35 subjects who had a common otogenic cause of vertigo by the diagnostic algorithm.
Therefore, it is desirable that machine learning methods are able to correctly classify compounds that are different from those present in the training set.
For the classifiers which use large numbers of carriers, however, seven of the eight tested were able to correctly classify over 95% of the test set examples.
In serum, the combination of three protein ion levels with m/z values of 4968, 9402 and 12,948 was able to correctly classify the treatment groups thus: 88% control, 100% acrylamide, 92% methylmercury.
The neural network obtained is able to correctly classify 81.6% of highly repetitive lifting industrial jobs as posing low or high-risk for LBDs, from five independent variables representing biomechanical risk factors.
