Sentence examples for be a better classifier from inspiring English sources

Further research is needed to determine which will be a better classifier for TNBC among Ki-67 and basal markers.

Total number of specific aberrations showed the largest AUC (0.814) in the ROC analysis compared with single aberrations and to 11 different combinations of these 4 aberrations thus seems to be a better classifier regarding metastatic tumours.

We were also interested in whether we would replicate previous work indicating that Random Forest is a better classifier than other Base and Ensemble classifiers [18].

As a conclusion, the FA is a better classifier if the segments are longer than 3 s which is a common fact because most of the segments are between 10 and 20 s long and a collar of 1 s is not scored around each reference boundary.

The hydrophobic core is a hallmark of transmembrane proteins hence a method that considers the hydrophobicity of residues is a better classifier of these structures.

However, when specifically applied to lesions classified by radiologists as Pi-RADS 3 (equivocal), the mp-MRI LR model was a better classifier than the T2-only LR model.

When we have no prior knowledge on the incoming data, it is difficult to determine which type of classifier is better, so combining multiple types of classifiers is likely to be a better solution than simply choosing either of them.

Thus the proposed method can be considered as a better classifier for healthy and medical problems and also when the great non-stationary data should be classified.

When classification accuracy (CA) is less than 100%, it is important to distinguish fundamentally unclassifiable cases (e.g., recently acquired sequences and slowly evolving traits) from boundary cases that could be distinguished by using a better classifier.

Furthermore, some suggestions are given for choosing a better classifier.

The idea of aggregating many simple classifiers to yield a better classifier is a widely used strategy in machine learning that capitalizes on the idea that using a set of classifiers that produce barely better results than random guessing can achieve arbitrarily high accuracy when combined appropriately.