Correct the definitions of precision and recall.

2-Regression/4-Logistic/README.md

@@ -237,9 +237,9 @@ As you might have guessed it's preferable to have a larger number of true positi
 
 Let's revisit the terms we saw earlier with the help of the confusion matrix's mapping of TP/TN and FP/FN:
 
-🎓 Precision: TP/(TP + FN) The fraction of relevant instances among the retrieved instances (e.g. which labels were well-labeled)
+🎓 Precision: TP/(TP + FP) The fraction of relevant instances among the retrieved instances (e.g. which labels were well-labeled)
 
-🎓 Recall: TP/(TP + FP) The fraction of relevant instances that were retrieved, whether well-labeled or not
+🎓 Recall: TP/(TP + FN) The fraction of relevant instances that were retrieved, whether well-labeled or not
 
 🎓 f1-score: (2 * precision * recall)/(precision + recall) A weighted average of the precision and recall, with best being 1 and worst being 0