Are entropy and cross-entropy the same thing as per basic definition?
If there is a difference:
Decision tree splits take on entropy or Gini index, can we use cross-entropy to split decision trees? OR should I use it as an evaluation metric after running the decision tree algorithm?
Also, does the decision tree algorithm assumes any distribution?
then how can we use the KL Divergence metric?
I am just trying to link a few concepts in a broader view.
These are my concerns with respect to the multiclass decision tree.