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KiKi · 2020年03月09日

问一道题:NO.PZ2015120204000030 [ CFA II ]

问题如下:

Paul suggests the following step which would be repeated every quarter.
Step 1 We apply ML techniques to a model including fundamental and technical variables (features) to predict next quarter’s return for each of the 100 stocks currently in our portfolio.
Then,
the 20 stocks with the lowest estimated return are identified for replacement.

Assuming regularization is utilized in the machine learning technique used for executing Step 1, which of the following ML models would be least appropriate:

选项:

A.

Regression tree with pruning

B.

LASSO with lambda (λ) equal to 0

C.

LASSO with lambda (λ) between 0.5 and 1

解释:

B is correct. It is least appropriate because with LASSO, when λ = 0 the penalty (i.e., regularization) term reduces to zero, so there is no regularization and the regression is equivalent to an ordinary least squares (OLS) regression.

A is incorrect. With Classification and Regression Trees (CART), one way that regularization can be implemented is via pruning which will reduce the size of the regression tree—sections that provide little explanatory power are pruned (i.e., removed).

C is incorrect. With LASSO, when λ is between 0.5 and 1 the relatively large penalty (i.e., regularization) term requires that a feature makes a sufficient contribution to model fit to offset the penalty from including it in the model.

老师,可以麻烦提供一下解题思路吗?没有看到考点,谢谢
3 个答案

星星_品职助教 · 2020年11月26日

@三金 

不矛盾。C正确的意思是C选项的描述正确。题目要选的是不正确的。

星星_品职助教 · 2020年04月20日

同学你好,这里面并不是要看股票的个数,而是要看分析的变量“next quarter’s return ”,return是个典型的连续型变量,所以不能用离散的分析方法。

 

星星_品职助教 · 2020年03月09日

同学你好,

题干问regularization的哪种方法最不合适。

选项A,pruning是CART里的一种regularization方法,所以不选。

B,C很类似,考察的是LASSO里如何能达到regularizaion的效果,如果λ=0的话,penalty term就起不到效果了,因为无论施加多少penalty,这个时候系数为0都会被消掉。这个时候LASSO也失效了,就退化成了一个一般的线性回归。

C正确,λ>0,有惩罚的效果,也就能达到regularization

我叫仙人涨 · 2020年04月19日

老师,这个A选项regression tree不是用来估计连续的么?题目说20个最低的,这个课后题的A选项,更严谨的说法应该是classification tree with pruning 对么

三金 · 2020年11月26日

老师,您的解答和答案矛盾了吧?