How can we calculate anomaly score using decision_function(X) One-Class-SVM

Question

I want to know how can I calculate anomaly score using decision_function(X) in One-Class SVM on my own data-set.

I have studied this post and also have seen this example:

Now I want to put my own data-set in the example given above. How is it possible for me. Thanks in advance.

Answer 1

It's simply more negative the decision function output is, more anomalous the datapoint is.

Refer here :

Signed distance to the separating hyperplane. Signed distance is positive for an inlier and negative for an outlier.