Key differences between CART (used in SKlearn) and ID3 are:
Type of learning:
- ID3, as an "Iterative Dichotomiser," is for binary classification only
- CART, or "Classification And Regression Trees," is a family of algorithms (including, but not limited to, binary classification tree learning).
Loss functions used for split selection.
- ID3, selects its splits based on Information Gain, which is the reduction in entropy between the parent node and (weighted sum of) children nodes.
- CART, when used for classification, selects its splits to achieve the subsets that minimize Gini Impurity.