Entropy and the Big Picture
The viewer learns what entropy is, why decision trees care about it, and the basic vocabulary needed before any split happens.
Entropy Makes Decisions Clear shows how uncertainty can be measured, then reduced by choosing the split that leaves the cleanest groups. By the end, you'll know: what entropy measures, why splits use it, and how decision trees choose. A decision tree starts with a simple problem: it has a mixed group of examples and needs to ask one question first. Entropy helps here because it tells you how mixed that group is before any split happens. If the group is very mixed, entropy is high. If most examples already share the same label, entropy is low. So the tree uses entropy to predict which question will separate the data into cleaner groups. Before we split anything, we need the pieces on the table. You have labeled data, which means each row already has a target answer, and features, which are the columns the tree can ask about. In a binary classification problem, the target has two possible labels. A node is one point in the tree where a question gets asked, and a leaf is where the tree stops and gives its final answer. When a feature is categorical, the split can separate values into groups, like yes and no. What makes a node feel messy is its class distribution. If the labels are split evenly, the node is uncertain. If one label dominates, the node is more pure. Entropy turns that mix into a number, and probability is what sits underneath that number. Information gain comes later, after a split is tested. It measures how much the split reduces impurity. So when you hear these terms together, keep the flow in mind: data enters, a question splits it, class distribution changes, entropy shifts, and information gain tells you whether the change was worth it.