Isolation Forest works as an anomaly detection approach and is based on the Random Forest algorithm. It assigns an anomaly score between 0 and 1 to each observation, where values close to 1 indicate the points are more likely to be outliers, and values closer to 0 are unlikely to be anomalies. At a high level, the intuition of the algorithm is that in the construction of a decision tree, points that are outliers are more likely to be partitioned into nodes that are a shorter path from the root node, since a decision tree splits a variable in such a way that creates the most differentiation between the observations that fall into different nodes in a tree.