Is accuracy = 1- test error rate Apologies if this is a very obvious question, but I have been reading various posts and can't seem to find a good confirmation.  In the case of classification, is a classifier's accuracy = 1- test error rate?  I get that accuracy is $\frac{TP+TN}{P+N}$, but my question is how exactly are accuracy and test error rate related.  
 A: In principle, accuracy is the fraction of properly predicted cases.
This is the same as 1 - the fraction of misclassified cases or 1 -  the *error* (rate).
Both terms may be sometimes used in a more vague way, however, and cover different things like class-balanced error/accuracy or even F-score or AUROC -- it is always best to look for/include a proper clarification in the paper or report.
Also, note that test error rate implies error on a test set, so it is likely 1-test set accuracy, and there may be other accuracies flying around.
A: @mbq answered: 

"1-the fraction of misclassified cases, that is error(rate)"

However, it seems wrong as misclassification and error are the same thing. See below (from http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/):
Accuracy: Overall, how often is the classifier correct?
(TP+TN)/total = (100+50)/165 = 0.91
Misclassification Rate: 
Overall, how often is it wrong?
(FP+FN)/total = (10+5)/165 = 0.09
equivalent to 1 minus Accuracy
also known as "Error Rate"
