Consider a dataset with $90$ negative examples and $10$ positive examples. Suppose a model built using this data predicts $30$ of the examples as positive $($only $10$ of them are actually positive$)$ and $70$ as negative. What are the numbers of True Positives $($TP$),$ False Positives $($FP$),$ True Negatives $($TN$),$ False Negatives $($FN$),$ Accuracy, Precision, Recall, and Specificity? Show all your steps.

$\backslash$table$[[,$Actual Label$],[,$Positive,Negative$],[$Predicted Label,True Positive $($TP$),$False Positive $($FP$),],[$Positive$,$False Negative $($FN$),$True Negative $($TN$)]]$