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reachqi · 2019年11月18日

问一道题:NO.PZ201512020300000503

* 问题详情,请 查看题干

问题如下:

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 cla
ssification or regression.

Step 2 的文字是否可以解读一下?从哪些描述可以得出是unsupervised learning? 我能读出是分类,但是无法判别是supervised or unsupervised learning.

1 个答案
已采纳答案

星星_品职助教 · 2019年11月18日

同学你好,

这道题其实并非是考察是supervised还是unsupervised,而是考察具体不同类的算法之间的辨析。从step 2的描述,可以看出是将1万只股票分成20个组,分组的原则是基于相似的财务和非财务指标。所以就是根据相似点分组。这个符合k means clustering的定义,可以回忆一下老师课上讲的“找大哥”的方法。

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.

K means这种算法属于聚类,并不涉及贴标签,所以就是unsupervised learning。但这个不是这道题的重点,如果要专门考察是不是非监督式学习,可能会出现是否有target variable,是否有labeled data set,是否有一个连续型的Y变量等方面进行考察。

相比较另外两个选项,PCA是降维,和描述无关;CART是分类树或者回归树,描述也不涉及一级一级的分类。所以可以很容易的排除。机器学习这个章节是典型的学起来很难,但是考察的并不难,加油~


 

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NO.PZ201512020300000503 老师好,根据题目信息,比较容易排除B。然后我就不知道A和C两个选哪一个。A我觉得也对,那我讲讲为什么我觉得C也对吧。 题干说根据一些财务和非财务的特征分成20个小组,其实我们用树的形式也可以分成20个组呀。> <麻烦老师帮忙讲下,谢谢。

2021-05-22 14:24 1 · 回答

老师请问Step 2里有这样的描述“financiannon-financicharacteristics”, 这个描述是否属于贴了标签呢?

2020-02-22 18:22 1 · 回答