The phrase "a maximum classification rate" is correct and usable in written English.
It can be used in contexts related to data analysis, machine learning, or any field where classification performance is being discussed.
Example: "The model achieved a maximum classification rate of 95%, indicating its effectiveness in accurately categorizing the data."
Alternatives: "highest classification rate" or "optimal classification rate".
Consequently one critical question arises and still needs to be answered: how to choose the set of features to able to reach the maximum classification rate for a given classification technique.
The optimum parameters are chosen when the maximum classification rate is achieved on a validation set.
The plot indicates that the maximum classification rate is obtained with a window length of 4.7 seconds.
The amount of latent components n for 1D PCA (methods 1 and 2) and 1D PLS (methods 3 and 4), as well as the number of time n C and frequency n r components for 2D PCA (methods 5 and 6) and 2D PLS (methods 7 and 8), was selected according to the maximum classification rate obtained.
Therefore, putative non-inhibitor training dataset can be generated by extracting a few representative compounds from each of those families that contain no known inhibitor, with a maximum possible "wrong" classification rate of <15% even when all of the undiscovered inhibitors are misplaced into the non-inhibitor class.
The maximum classification accuracy rate of 98.6% is achieved with 50 signals of 51 normal and 159 out of 161 pathological signals are correctly classified.
A 30-miRNA signature was developed by class prediction and clustering, which reached the maximum correct classification rate (100%) for ICC and NIBD tissues.
(a)Maximum annual rates of improved pension.
This technique is also illustrated in Fig. 7. Fig. 8 shows the minimum and maximum overall classification rates obtained by our feature extraction methods, for all different combinations of DC methods and interpolation methods.
The curves depict the correlation between a false positive rate and a correct classification rate.
