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+RNN = Recurrent Neural Network
+
+two passes
+
+
+Precision=TP/(TP+FP) - false positive -> 0
+Recall=TP/(TP+FN) - tp -> 0
+F1=2Precision⋅Recall​/(Precision + Recall)
+Accuracy = (TP+TN)/(TP+TN+FP+FN)
+
+
+false positive false negative
+
+true pred
+
+
+FP A = pred = A  true != A
+FN A = true = A  pred != A