I'm currently working on an unsupervised anomaly detection project, and for it I'm using IsolationForest through scikit-learn. My question is, why/how is it possible for the model to predict something to be an anomaly when it is within the decision function space for inliers?
I've attached my results here:
Could the size of the decision function space be due to my input dimension vs this 2 dimensional projection?
I also made a quick plot of anomaly score vs. prediction (0 = inlier, 1 = anomaly):
As seen, there exists outliers above the threshold score, which doesn't make sense to me. Can someone explain?