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Answer: When dealing with large datasets or complex models and computational efficiency is a priority.
The Train-Test Split is considered more efficient than Cross-Validation in scenarios where computational resources and time are limited. This is particularly relevant for large datasets or complex models, as Cross-Validation requires the model to be trained multiple times (k times for k-fold Cross-Validation), leading to higher computational costs and longer evaluation times. In such cases, a single Train-Test Split can provide a quicker, albeit sometimes less robust, evaluation of the model's performance.
Author: LeetQuiz Editorial Team
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Under which circumstances is the Train-Test Split method preferred over Cross-Validation for evaluating machine learning models?
A
When the model's interpretability needs to be maximized.
B
When there's an abundance of computational resources and time.
C
When dealing with large datasets or complex models and computational efficiency is a priority.
D
When the training process can be significantly accelerated through parallelization.
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