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finger · 2021年03月18日

关于惩罚项的问题

* 问题详情,请 查看题干

NO.PZ201512020300000502

问题如下:

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 Cla
ssification 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.

这道题不是很理解,不应该是等于0才能够让惩罚项更有意义吗

1 个答案
已采纳答案

星星_品职助教 · 2021年03月18日

同学你好,

LASSO中penalty term的公式如下:


可以看出,如果λ=0的话,这个penalty term就起不到效果了。因为无论有多少个自变量,无论系数bk会增加到多少,由于前面的λ=0,这个penalty term都会被消掉。这个时候LASSO也就失效了。

此时方程就退化成了一个一般的线性回归。所以B选项是做不了regularization的。

所以只有当λ>0时才会有惩罚的效果,也就可以达到regularization目的。即C选项。

附讲义供参考。