Sentence examples for a positive class from inspiring English sources

A post-processing technique for Support Vector Machine (SVM) algorithms for binary classification problems is introduced in order to obtain adequate accuracy on a priority class (labelled as a positive class).

The threshold value is "0" to classify DA values with a positive class or a negative class.

We train the SVM to predict two classes, a positive class for a spoken text utterance and a negative class for a written one.

They also defined Precision as "the probability that a predicted positive class instance is a true positive" and explained Recall as "the probability of success in recognizing a positive class instance".

The threshold value is "0" to classify DA values with a positive class or a negative class Fig. 3 Discriminant Analysis with 2 classes by PLS regression formula in Dataset No. 4. This shows a result of the first grouping of the data in Dataset No. 4 shown in Table 2 by discriminant analysis with 2 classed using PLS regression formula.

Thus, TP means that an instance of positive class is correctly classified as positive (cautious driving evidence), FN means that a positive class instance is misclassified as negative (reckless driving evidence), TN means that a negative class instance is classified correctly as negative, and FP means that a negative class instance is misclassified as positive.

Thus, in this step, we first learn the low dimensional embedding using a set of spikes annotated by an expert (positive class), and a set of spike-like waves that are nonspecific sharp transients (negative class).

Sensitivity is a statistical measure of how well a binary classification test correctly identifies a condition (positive class; relevant predictors).

The results are shown in Figure 2. LP formulations based on a weaker positive class of linear functions (cross) and a general class of functions linear (diamond) produce similar results.

It occurs when the classes represented in a problem show a skewed distribution, i.e., there is a minority (or positive) class, and a majority (or negative) one.

The task of predicting the functional class of a protein or peptide can be considered as a two-class (positive class and negative class) classification problem for separating members (positive class) and non-members (negative class) of a functional or interaction class.