NO.PZ2015120204000032
问题如下：
Paul suggests the following step which would be repeated every quarter.
Step 2 We utilize ML techniques to divide our investable universe of about 10,000 stocks into 20 different groups, based on a wide variety of the most relevant financial and non-financial characteristics. The idea is to prevent unintended portfolio concentration by selecting stocks from each of these distinct groups.
The hyperparameter in the ML model to be used for accomplishing Step 2 is?
选项：
A.100, the number of small-cap stocks in Alef’s portfolio.
10,000, the eligible universe of small-cap stocks in which Alef can potentially invest.
20, the number of different groups (i.e. clusters) into which the eligible universe of small-cap stocks will be divided.
解释：
C is correct. Here, 20 is a hyperparameter (in the K-Means algorithm), which is a parameter whose value must be set by the researcher before learning begins.
A is incorrect, because it is not a hyperparameter. It is just the size (number of stocks) of Alef’s portfolio.
B is incorrect, because it is not a hyperparameter. It is just the size (number of stocks) of Alef’s eligible universe.
20 is a hyperparameter (in the K-Means algorithm), which is a parameter whose value must be set by the researcher before learning begins.
这么说，有两个超参数吗？K和20？
