Building a Decision Tree from scratch. Uses Gini Impurity as a measure.
Gini impurity is a measure of misclassification, which applies in a multiclass classifier context.
Gini impurity at a node is the is the chance a randomly selected data point and a randomly selected target label in the dataset are incorrect match - misclassification.
Gini coefficient applies to binary classification and requires a classifier that can in some way rank examples according to the likelihood of being in a positive class.
https://techdevguide.withgoogle.com/resources/ml-recipes-with-josh-gordon/#!