The Gini Index is a measure of impurity or inequality in statistical and monetary settings, and it is used as an impurity measure in decision tree algorithms for classification tasks in machine learning. The Gini Index measures the probability of a randomly picked test being misclassified by a decision tree algorithm, and its value ranges from 0 (perfectly pure) to 1 (perfectly impure). The Gini Index is calculated by subtracting the sum of the squared probabilities of each class from one. It favors larger partitions and is easy to implement, whereas information gain favors smaller partitions with distinct values. A feature with a lower Gini index is chosen for a split. The classic CART algorithm uses the Gini Index for constructing the decision tree.