28. In machine learning, a confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. One can use the matrix to calculate the evaluation metrics, such as accuracy, precision, recall, and F1 score. Suppose a confusion matrix contains TP (true positive) = 900, FN (false negative) = 100, FP (false positive) = 50, and TN (true negative) = 950. What is the value of precision for the model? (A) 1850/2000 (B) 900/1000 (C) 900/2000 (D) 50/950 (E) 900/950