CART较于KNN的优势

问题如下：

Paul suggests the following step which would be repeated every quarter.

Step 3 For each of the 20 different groups, we use labeled data to train a model that will predict the five stocks (in any given group) that are most likely to become acquisition targets in the next one year.

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

B.

Statement III only

C.

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.