Precision tell us the fraction of positive predictions that are correct. It takes into account only the positive class (TP and FP - second column of the confusion matrix), as is stated in the following formula:
Recall measures the fraction of correctly identified postive instances. It considers parts of the postive and negative classes (TP and FN - second row of confusion table). The formula of this metric is presented below:
In this problem, the precision and recall values were 67% and 54% respectively. So, these measures reflect some errors of our model that accuracy did not notice due to the class imbalance.
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