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渔夫阿哲 · 2021年02月22日

这道题看不懂,能不能请老师解释一下

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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.

这道题看不懂,能不能请老师解释一下

1 个答案

星星_品职助教 · 2021年02月23日

同学你好,

题干问如果这个机器学习的算法要做regularization,选择里的哪种方法最不合适。

选项A,pruning是CART里的一种regularization方法,所以可以做regularization。

B,C很类似,考察的都是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。所以B选项是做不了regularization的。

C选项中的λ>0,此时有惩罚的效果,也就可以达到regularization目的。

所以只有B选项做不到regularization,即“ least appropriate”

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建议提问时具体说一下哪个点不懂,问题是什么(e.g.答案解析某个点不明白/题干某句话看不懂/选项里的某个知识点遗忘等)

不然助教不知道重点应该去强调哪个方面的。