Sure thing – let’s peel back the layers. At its core a decision tree under uncertainty is just a way to map out choices when the outcomes aren’t crystal clear. We start by asking, “Which feature most splits the data into purer groups?” that’s entropy or Gini. Once we pick a split, we recurse, branching down until the leaves are as homogeneous as we can make them. The algorithm you’re talking about probably adds a twist: it iteratively refines those branches, pruning over‑fitted nodes and re‑weighting probabilities as new information arrives, turning the raw chaos of raw data into a tidy, predictable structure. The key is that each split is a decision under uncertainty, and the tree as a whole is a scaffolding that organizes those decisions into a coherent whole.