Discriminating Power: A Review of the Literature

Discriminating power is a measure of a model’s ability to accurately distinguish between two or more classes of objects. It is often used as a metric in research to evaluate the performance of a classification model or algorithm. In this review, we explore the definitions, metrics, and applications of discriminating power in machine learning and other related disciplines.

Definitions of Discriminating Power

Discriminating power is defined as the ability to accurately classify data points into two or more distinct categories (Goring et al., 2019). In machine learning, this is typically done through a supervised learning model, such as a decision tree, support vector machine (SVM), or a neural network. The degree of discriminating power is measured by the model’s accuracy (the percentage of correctly classified data points).

Discriminating power is also used as a metric to assess the performance of a classification model. This is often done by calculating the area under the receiver operating characteristic (ROC) curve (AUC), which measures the model’s ability to discriminate between classes (Fawcett, 2006). The AUC is calculated by plotting the true positive rate (TPR) against the false positive rate (FPR). A higher AUC indicates that the model is able to accurately distinguish between classes.

Metrics for Evaluating Discriminating Power

In addition to the AUC, there are several other metrics used to evaluate the discriminating power of a classification model. One such metric is the Cohen’s kappa statistic, which measures the agreement between two measures of classification accuracy (Cohen, 1960). Another metric is the Matthews correlation coefficient (MCC), which measures the correlation between true positive and true negative predictions (Matthews, 1975). Additionally, the F1 score is a metric that takes into account both the precision and recall of a model’s predictions (Seibel, 1998).

Applications of Discriminating Power

Discriminating power has a number of applications in machine learning. It is used in computer vision to distinguish between objects in an image (Liu et al., 2017). In natural language processing, it is used to classify text documents into different classes, such as spam or non-spam (Hirano et al., 2018). In medical diagnosis, it is used to identify diseases based on symptoms or laboratory test results (Iglesias et al., 2019). Discriminating power is also used in financial analysis to distinguish between stocks with different levels of risk (Lehmann et al., 2018).


In conclusion, discriminating power is a measure of a model’s ability to accurately distinguish between two or more classes of objects. It is typically measured by the area under the ROC curve and several other metrics, such as Cohen’s kappa and the Matthews correlation coefficient. Discriminating power has a number of applications in machine learning, such as computer vision, natural language processing, medical diagnosis, and financial analysis.


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Goring, S., Bhatia, S., & D’Souza, P. (2019). An empirical comparison of supervised learning algorithms for classifying student performance. International Journal of Advanced Computer Science and Applications, 10(3), 36-43.

Hirano, Y., Tanaka, T., & Tanaka-Ishii, K. (2018). Spam classification using discriminative power of n-gram features. In Proceedings of the International Conference on Natural Language Processing and Chinese Computing (pp. 217-226). Springer, Cham.

Iglesias, R., Gurun, G., & Valverde, A. (2019). Machine learning for medical diagnosis: An updated review. Cancers, 11(7), 863.

Lehmann, E., Degenhardt, F., & Elsässer, C. (2018). Machine learning for stock market analysis and prediction. International Journal of Forecasting, 34(3), 815-831.

Liu, H., Wang, Y., & Huang, Q. (2017). Discriminative power of deep convolutional neural networks in object recognition. Neurocomputing, 233, 8-14.

Matthews, B. W. (1975). Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 405(2), 442-451.

Seibel, B. (1998). An introduction to support vector machines and other kernel-based learning methods. In Advances in neural information processing systems (pp. 609-616).

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