What does SVM predict in Analytics and Support Vector Machine learning?

Support Vector Machines (SVM) are a type of supervised learning model primarily used for classification tasks within analytics. The key feature of SVM is that it identifies a hyperplane in a multi-dimensional space that best separates different classes of data points. The distance of a data point from this hyperplane is crucial because it indicates how confidently that point belongs to one class over another.

When making predictions, SVM assesses the position of data points relative to this hyperplane. Thus, option A accurately describes that SVM predicts the relevance of uncoded documents based on their distance to the hyperplane. In practical terms, documents that are closer to the hyperplane may be more ambiguous or relevant as they lie near the boundary of classification, while those that are far from the hyperplane are more distinctly classified.

The other options do not align with the fundamental purpose and functioning of SVM in the context of document analysis. The relevance of uncoded documents is a central theme in the application of SVM, making option A the most accurate representation of what SVM predicts in analytics.