I've been using isolation forest for anomaly detection, and reviewing its parameters at scikit-learn (link). Looking at "bootstrap", I'm not quite clear what using bootstrap would cause. For supervised learning, this should reduce overfitting, but I'm not clear what the effect on anomaly detection should be.
I think it would require the trees to achieve more "consensus" about what the anomaly is, therefore, reducing the effect of any single feature. I.e, an anomalous observation would probably need to be anomalous consistently and over a number of features (?).
Is this a correct interpretation of this parameter?
max_features
, but not really forbootstrap
. (I can't think of a particularly clear effect forbootstrap
, except that it effectively decreases the sample size for a givenmax_samples
.) $\endgroup$