-Gini is intended for continuous attributes, and Entropy for attributes that occur in classes (e.g. colors
-“Gini” will tend to find the largest class, and “entropy” tends to find groups of classes that make up ~50% of the data
-“Gini” to minimize misclassification
-“Entropy” for exploratory analysis
-Some studies show this doesn’t matter – these differ less than 2% of the time
-Entropy may be a little slower to compute
