Question at position 26 True/False Question: Forward selection is faster than backward selection if few features are relevant to prediction. TrueFalse

Question

BlackTom AI · Accepted Answer

Starting with the question, we’re comparing forward selection versus backward selection in feature selection when only a small subset of features is actually relevant.
Option 1: True. In forward selection, you begin with no features and iteratively add one feature at a time, stopping when adding more features does not improve the model markedly. If only a few features are truly informative, this process tends to examine a smaller portion of the feature space and converge quickly because many candidate features are discarded early as they don’t provide enough incremental benefit. In contrast, backward selection starts with all available features and removes them one by one; this can be more computationally intensive when the total feature set is large, since you must evaluate many models that include all features at the start and then prune. Therefore, when only a few features are relevant, forward selection is typically faster overall.
Option 2: False. This statement would be true only if forward selection were inherently slower or equal in cost regardless of the number of relevant features, which contradicts the usual behavior described above. It ignores the practical computational savings gained when the relevant feature set is small and the method can stop early or avoid evaluating many uninformative features.
In summary, the key idea is that the cost of backward selection grows with the total number of features because it starts from a full model and prunes, whereas forward selection can efficiently home in on the small set of useful features, making it faster when few features matter. This reasoning supports the claim that forward selection is faster in that scenario, while the alternative highlights a scenario that does not align with typical practice.

Question at position 26 True/False Question: Forward selection is faster than backward selection if few features are relevant to prediction. TrueFalse

查看解析

登录即可查看完整答案

类似问题

Refer to the feature importance graph below. If you want to simplify the model with minimal loss in predictive power, which features could you REMOVE so that the remaining features still account for at least 80% of the total importance? [2 marks]

If a feature has the same value for all records, then:

Question at position 10 Select all correct answers: Which of the following are common methods of feature selection? kNNPrincipal Component Analysis (PCA)Ridge RegressionMin Max ScalingBackward EliminationLasso RegressionForward Selection

Feature selection increases the risk of overfitting

Feature selection increases the risk of overfitting

For doing Feature Selection, there is a Filtering approach and a Wrapper approach. Highlight what is true.

Term j has good discriminating properties and therefore can be a good feature if it creates an imbalanced distribution of class labels for documents that contain term j:

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