问题如下：
Comparing two ML models that could be used to accomplish Step 3, which statement(s) best describe(s) the advantages of using Classification and Regression Trees (CART) instead of K-Nearest Neighbor (KNN)?
Statement I For CART there is no requirement to specify an initial hyperparameter (like K)
Statement II For CART there is no requirement to specify a similarity (or distance) measure
Statement III For CART the output provides a visual explanation for the prediction
选项：
A.Statement I only
Statement III only
Statements I, II and III
解释：
C is correct. The advantages of using CART over KNN to classify companies into two categories (“not acquisition target” and “acquisition target”), include all of the following: For CART there are no requirements to specify an initial hyperparameter (like K) or a similarity (or distance) measure as with KNN, and CART provides a visual explanation for the prediction (i.e., the feature variables and their cut-off values at each node).
A is incorrect, because CART provides all of the advantages indicated in Statements I, II and III.
B is incorrect, because CART provides all of the advantages indicated in Statements I, II and III.
CART法不是为了防止overfitting的问题，可以有两种方法，一种是事前设定regularization parameters，还有一种是事后pruning。第一种不就是事前先设参数吗？为什么这个题说，事前不用设hyperparameter？
