Sentence examples for binary classification in which from inspiring English sources

MCC or Matthews correlation coefficient [36] is a measure of the quality of a binary classification, in which variable to be predicted has two values only.

Let us suppose a two-class prediction problem (binary classification), in which the outcomes are labeled either as positive (p) or negative (n) class.

This is due to the fact that nationality prediction is a binary classification problem in which even a random prediction would score 50%, whereas age range and nationality detection are respectively seven- and eight-class classification problems in which a random classifier would only score 14% and 12%, respectively.

Basically, the problem is modeled as a binary classification problem, in which the goal of the SVM-based methods is to find a hyperplane that separates the relevant from the non-relevant images.

Similar to [ 20], we cast peptide identification as a binary classification problem in which "good" PSMs are labeled as "+1" and "bad" PSMs are labeled as "-1".

The DET curve is a plot of error rates for binary classification systems, in which the lower left curve implies the better performance.

We formulate it as a binary classification problem, in which "good" PSMs are assigned to class "correct" or "+1" and "bad" PSMs to class "incorrect" or "-1".

We formulate the task of predicting acceptor splice sites as a binary classification problem in which the positive class represents true acceptor splice sites and the negative class is comprised by decoy "AG" sites.

In a first subsection, Section 4.1, evaluation measures are defined, which correspond to well-known measures in e.g. binary classification in Machine Learning.

Since the standard SVM is tailored for binary classification, in multiclass data sets we used the one-versus-all (OVA) [ 26] approach, which firstly solves many binary problems and then combines the results to solve the multiclass problem.

Concept learning in DLs is similar to binary classification in traditional machine learning.