I've been using the scikit learn
sklearn.ensemble.IsolationForest implementation of the isolation forest to detect anomalies in my datasets that range from 100s of rows to millions of rows worth of data. It seems to be working well and I've overridden the
max_samples to a very large integer to handle some of my larger datasets (essentially not using sub-sampling). I noticed that the original paper (https://ieeexplore.ieee.org/abstract/document/4781136) states that larger sample sizes create risk of swamping and masking.
Is it okay to use the isolation forest on large sample sizes if it seems to be working okay? I tried training with a smaller
max_samples and the testing produced too many anomalies. My data has really started to grow and I'm wondering if a different anomaly detection algorithm would be better for such a large sample size.