Explain the concept of entropy in decision trees.

Entropy is a measure of impurity or disorder used in decision trees to determine how to split the data. A node with high entropy means the data at that node is impure (mixed classes), while low entropy means the data is more homogeneous (mostly one class). The decision tree algorithm tries to minimize entropy at each split, increasing homogeneity in the resulting branches.