A large tree will generally have trouble capturing the structure in the data, while a small tree will have high variance.

Question

BlackTom AI · Accepted Answer

The statement says: a large tree will generally have trouble capturing the structure in the data, while a small tree will have high variance.

First, consider the behavior of decision trees in terms of bias-variance tradeoff. A large (complex) tree tends to fit the training data very closely, which reduces bias but increases variance. This means it captures even noise as if it were signal, leading to overfitting rather than trouble capturing structure; it typically models complex structure well on training data, though it may fail to generalize.

Second, a small (simple) tree tends to underfit the data, resulting in high bias because it cannot capture the underlying relationships. However, its simplicity generally yields lower variance, since it does not react strongly to fluctuations in the training data.

Therefore, the claim that a large tree will generally have trouble capturing the structure in the data is inaccurate; the more common issue with large trees is overfitting and high variance, not underfitting. Conversely, the assertion that a small tree will have high variance is also incorrect because small trees typically exhibit lower variance due to their simplicity, even though they have higher bias.

In summary, both parts of the statement mischaracterize the usual bias-variance dynamics of decision trees, making the overall claim false.

QBUS6810 (ND) Week 9 Quiz

A large tree will generally have trouble capturing the structure in the data, while a small tree will have high variance.

查看解析

登录即可查看完整答案

类似问题

Which statement is correct about bias, variance, and generalization error evaluated on the same problem?

Which of the following are true about model variance?

Select the following trends of errors that occur as a neural network grows in complexity

Question at position 13 Multiple Choice Question: A fitting curve plots Generalization performance vs. model complexityTrue positive rate vs. false negative rateTrue positive rate vs. false positive rateGeneralization performance vs. size of training set

Fill in Blanks. Choose among Overfitting and Underfitting: What does Blank A represent: [Fill in the blank], What does Blank B represent: [Fill in the blank],

更多留学生实用工具

考试浏览器助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单