# Interpretation of scikit-learn one class svm scores

How can I interpret the scores generated by the function score_samples(X) from a scikit-learn OneClassSVM model? Is there a way to tell when one sample is "more anomalous" than other? The predict() and decision_function() functions have sign information, yet the score_samples function does not have this.

from sklearn.svm import OneClassSVM

X = [[0], [0.44], [0.45], [0.46], [1]]
clf = OneClassSVM(gamma='auto').fit(X)

clf.predict(X)
# array([-1,  1,  1,  1, -1])

clf.score_samples(X)
# array([1.7798..., 2.0547..., 2.0556..., 2.0561..., 1.7332...])
$$$$


• Are these values used for the end outlier/not-outlier results that the predict` method outputs? if so, how are these used? – ElBrocas Jun 22 '20 at 15:56