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For each reaction centre a SVM binary classification model was trained and the score obtained for each potential SoM in a query molecule was used to rank the candidate reaction centres.

The genes that contributed most to a PLS binary classification model were BRCA1, DAPK1, MSH2, CDKN2A, PGR, PRKCDBP, RANKL.

A binary classification model is trained over the datasets of positive and negative samples, whose numbers are P and N, respectively.

Three binary classification models were developed using three techniques Decision Tree (DT), Deep Learning (DL), and Naïve Bayes (NB).

Binary classification models were developed to distinguish complementary from noncomplementary DNA-RNA target hybrids.

To probe the structural characteristics influencing on the SAR, the classification model was generated using a binary QSAR approach termed recursive partitioning (RP) analysis.

A jackknife-based supervised learning method called paired-samples test algorithm (PST), coupled with a binary classification model based on linear regression, was proposed and applied to two well known and challenging datasets consisting of 14 (GCM dataset) and 9 (NC160 dataset) tumor types.

The relationship shown in the figure can be illustrated by Equation 5 where the predicted probability of an aggregated model using three random binary classification models of equal accuracies is a quadratic function of the accuracy of the models Avar.

In a binary classification model, the closer the class predicted probability is to 0.0 or 1.0, the more likely the sample is to belong to that particular class.

By so doing, we are making the following original contributions: (1) Typically, a boosting classifier is trained as a binary classification model.

A more biologically meaningful approach would be to build a binary classification model that can automatically classify a gene as disease or not.