I am running several machine learning classifiers to predict something from my data. If I visualized the precision and recall tables as a result, is it enough to get clear idea about the proposed mdel? or do I have to consider the accuracy table in order to cover the issue?I am running 3 different classifiers (svm, perceptron, and isolation forest) to detect anomalies in post arrays according to 3 groups of features sets extracted from the texts, so far I have the results for each classifier according to 1 and 2 features sets and 1 and 3 and for all together. What I want to know is which classifier perform better(classify the text arrays to compromised or not ) with those combined extracted features, so far, I recognized that specific classifier with specific combined features has the highest precision value, but do I have to visualize the result of the recall and accuracy? Am I missing somehting? What precisely do you mean by perform better? Better in what sense? perform better means which classifier has the highest accuracy in classifying the text arrays to compromised text or not. 
(Percision & recall) Vs (Accuracy)? which one do I have to consider?
machine learning;classification;svm;perceptron