Sentence examples for binary classification can be from inspiring English sources

After the feature extraction step, binary classification can be used to discriminate spoofing attacks from real access attempts.

All models except CvBoost, which is solely for binary classification, can be used with any dimensionality of the response variable.

The classification result of an instance in a binary classification can be fall into four categories: true positive (TP), false positive (FP), true negative (TN) and false negative (FN).

Technically, the data-setup of our experiment for the binary classification can be rephrased as { x n, y n } n = 1 N where x n ∈ R d (d is the number of features and N is the number of patients) and y n ∈ { - 1, 1 }.

For a binary class problem, the costs of classification can be given in a cost matrix (Table  4).

The classification can be done on binary class data or multi-class data.

Classifications can be deceptive.

Classifications can be controversial.

This binary classification problem can be extended to multiclass using either the one versus one or the one versus all approach.

MCC is a commonly applied performance evaluation metric to measure the quality of binary classification and can be calculated according to the Eq. (1).

This estimator provides directly comparable results among the models because any binary classification system can be used to calculate ROC curves and to determine the precision of a diagnostic test.