NO.PZ201512020300000506
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.
老师好，
这道题statement I我是纠结了一下，因为对CART来说，可以设定一些超参数，也可以不用。所以我没判断出来到底是对是错。然后就去看了StatementII这个结论。
在StatementII里面，它说CART没有对similarity和distance的设定，我就在想，咱们CART里的root node和decision node，不都是用的是similarity吗？比如"ROE>10?"作为一个node下面有两个分支。其实在我看来就是一种similarity的设定。我是哪里又理解错了吗？ （总感觉我和出题人的思路总是不一样，哎><） 谢谢老师
