Calculating anomaly score for Anomaly detection us

Question

I have a question about Calculating anomaly score for Anomaly detection using One-Class SVM. My question is that: How can I calculate it using decision_function(X), just the way I calculate anomaly score in Isolation forest? Thanks a lot,

Answer 1

Yes, you have to use decision_function() as the measure of anomaly score in one class SVM.

Have a look at this example, you might get better understanding.

clf.decision_function(X_test)
# returns the signed distance to the separating hyperplane.
# Signed distance is positive for an inlier and negative for an outlier.

Answer 2

In Isolation Forests, anomaly score is a measure of the deviation of average length of the path required to single out a particular observation from the average length of path required to single out a "normal" observation

The average here is taken over all the different trees that are used. Since SVM is not an ensemble method - this notion of anomaly score does not directly apply.

One way, and I don't know how statistically/scientifically sound this is, of measuring an anomaly score is to build multiple SVM classifiers based on a subset of predictors. You could then use the percentage of times a particular point is classified as an outlier as a proxy for an anomaly score.