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little_back · 2020年01月25日

问一道题:NO.PZ2015120204000031

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.

Which of the following machine learning techniques is most appropriate for executing Step 2:

选项:

A.

K-Means Clustering

B.

Principal Components Analysis (PCA)

C.

Classification and Regression Trees (CART)

解释:

A is correct. K-Means clustering is an unsupervised machine learning algorithm which repeatedly partitions observations into a fixed number, k, of nonoverlapping clusters (i.e., groups).

B is incorrect. Principal Components Analysis is a long-established statistical method for dimension reduction, not clustering. PCA aims to summarize or reduce highly correlated features of data into a few main, uncorrelated composite variables.

C is incorrect. CART is a supervised machine learning technique that is most commonly applied to binary classification or regression.

based on a wide variety of the most relevant financial and non-financial characteristics这句话不就是说的是label吗?

1 个答案

星星_品职助教 · 2020年02月03日

同学你好,

label指的是指出什么是X什么是Y。题干中这句话只是一个粗略的分类标准,并没有提供有效的信息。