Most outlier detectors start by describing what "normal" looks like. They fit a density, or measure how far every point sits from its neighbours, and then flag whatever falls outside the comfortable middle. Isolation Forest throws that idea out. It never models normal at all. It just tries to cut each point off from the rest with random slices, and asks one question: how many cuts did that take?
The intuition is almost too simple. Picture a dense blob of points with a couple of strays sitting way off on their own. Draw a random line through the plane. Draw another. Keep slicing, each time keeping only the region a point lives in, until that point is completely alone in its own little cell. A stray out in empty space gets fenced off in one or two cuts, because there is nothing around it to protect it. A point buried in the middle of the crowd needs many cuts, because you have to keep whittling the crowd down before it is finally on its own.
That count of cuts is called the path length, and it is the whole signal. Short path means the point was easy to isolate, which means it was already sitting apart from everyone, which means it is probably an anomaly. Long path means it was deep in the crowd, which means it is normal. You do not need distances, you do not need a density estimate, you do not need to pick a radius. You count cuts.
