Sentence examples for evaluating classification from inspiring English sources

As performance metric, ROC (Receiver operating characteristics) plots are commonly used for evaluating classification performance, in which TPR (true positives rate) on the Y-axis is plotted as a function of FPR (false positives rate) on the X-axis.

We expand the example introduced in Fig. 1 in order to clearly explain the differences between the proposed method and the standard metrics frequently used when evaluating classification results.

The performances are tested on two pathological speech datasets, the NKI CCRT Speech Corpus (advanced head and neck cancer) and the TORGO database (cerebral palsy or amyotrophic lateral sclerosis), by evaluating classification accuracy without overlapping subjects' data among training and test partitions.

Multivariate modeling evaluating classification of breast tumors arising in older women by oncogenic pathway clustering and important clinico-pathologic variables was additionally performed.

The AUC metric has been widely used for evaluating classification methods, since it can provide a useful summary statistics of the classification performance over the entire range of specificity and sensitivity values.

Finally, the averaged correct rate from all processes represented the accuracy level for evaluating classification performance.

As early as in 1952, Chernoff [1] used the -divergence to evaluate classification errors.

A variation of F1, namely, Macro-F1, is normally reported to evaluate classification effectiveness on skewed datasets.

However, in the case of simulation we can evaluate classification accuracy using established indices (Ruopp et al. 2008).

The aim of this study was to evaluate classification algorithms based on ASOCT measurements for the detection of gonioscopic angle closure.

The main goal of this paper is to develop a two-step approach to evaluate classification algorithms for financial risk prediction.