An interesting contribution from Joel Anderson

Pipeline Risk algorithms can take in hundreds of variables and perform hundreds more calculations to determine the probability of failure for a given segment of pipe. Trying to determine what threats are biggest contributor to a system’s risk or even over the length of a given line can be challenging. A given threat might be high in one location and then exceeded by another a short distance away. It can be compared to trying to predict the height of waves that are rising and falling, seemingly at random. Trying to do this by just looking over a large table of numbers is a exercise in frustration and tedium. This is further complicated by the fact that it can be the interaction of threats that drive the overall probability of failure (PoF).

See the full post here: Decision_Trees