Consider a binary classification problem where a model has been trained to distinguish between two classes: Positive and Negative. The confusion matrix of the model on a test dataset is given below. Calculate the following metrics:

a$)$ Accuracy for the positive class

b$)$ Precision for the Positive class

c$)$ Recall $($Sensitivity$)$ for the Positive class

d$)$ F$-1$ Score for the Positive class

Definitions: Precision $-$ is the ratio of correctly predicted positive observations to the total predicted positives. Recall $-$ is the ratio of correctly predicted positive observations to all the observations in the actual class.