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The isolation forest is a tree-based algorithm, built around the theory of decision trees and random forests.

The algorithm divides the data into two parts based on a random threshold value.

This process continues recursively until each data point is isolated.

The algorithm loops through the entire data set and filters out data points that took fewer steps than others to isolate them.

In most unsupervised methods, data points are first profiled and anomalies are flagged if they do not resemble this profile.

Isolation forest, on the other hand, takes a different approach, it explicitly isolates anomalous data points.

It is important to mention that Isolation Forest is an unsupervised machine learning algorithm.