• given training set, in cv, test, predict whether examples anomaly or not
  • similar as supervised learning give labeled data
  • Mind that because most of the data are normal examples, the graph itself tend to shape skew-class (y = 0) for classification
  • consider y = 0 all the time will give too high accuracy, so classification may not recommended
  • The good way for evaluating it, is choose one of the alternative in possible evaluation metrics. We may want to choose epsilon manually, and test it in cv test that maximize F-1 score, or at least it does well in cv test
  • When epsilon doing well in cv test, and learning behave correctly, finally test the algorithm in the test set
  • Process and evaluate Anomaly Detection in number F-1 score will be make time more efficient
  • We also learned how to use labeled data for Anomaly Detection, similar as supervised learning
  • Next, when to choose Anomaly Detection wisely, or when choosing Anomaly Detection vs Supervised Learning