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?