Consider the following result from a classification problem and a model built for it. Predicted Yes Predicted No Total Actual Yes 223 43 266 Actual No 87 453 540 Total 310 496 806 1. What is the value of the following metrics given the result table above? Accuracy Precision Recall F1-score 2. Sometimes, the cost of errors is not constant. For example, if we were predicting whether a patient has cancer, a false negative could be considered much worse than a false positive. If that were the case, which of the above statistics would you want to optimize? Briefly explain why.