A simple explanation of Gini Impurity and how it’s used to train decision trees. In brief:

Gini Impurity is the probability of incorrectly classifying a randomly chosen element in the dataset if it were randomly labeled according to the class distribution in the dataset

Click through for the full break down and some helpful examples.