How does decision tree splitting work in data mining?

Decision trees split data based on feature conditions that maximize purity.

At each node, algorithms evaluate all possible splits using metrics like Gini impurity or entropy. The best split is the one that most reduces impurity. This process is recursive until stopping criteria are met, such as maximum depth or minimum samples per leaf.