Mike Caruana

… per product, the allocation of data scientists can easily be determined as a proportion. I explain more here.

At each node, the decision tree searches through the features for the value to split on that results in the greatest reduction in Gini Impurity. (An alternative for splitting nodes is using the information gain, a related concept).

…is point it’ll be helpful to dive into the concept of Gini Impurity (the math is not intimidating!) The Gini Impurity of a node is the probability that a randomly chosen sample in a node would be incorrectly labeled if it was labeled by the distribution of samples in the node. For example, in the top (root) node, there is a 44.4% chance of incorrectly classifying a data poi…