QuestionJuly 2, 2025

The basic idea is that if a variable is strongly predictive, then it will likely be predictive no matter how the data is "sliced"or split up. This most specifically refers to machine learning randomization. regularization. cross-validation.

The basic idea is that if a variable is strongly predictive, then it will likely be predictive no matter how the data is "sliced"or split up. This most specifically refers to machine learning randomization. regularization. cross-validation.
The basic idea is that if a variable is strongly predictive, then it will likely be
predictive no matter how the data is "sliced"or split up. This most specifically
refers to
machine learning
randomization.
regularization.
cross-validation.

Solution
4.3(288 votes)

Answer

cross-validation. Explanation 1. Identify the Concept The concept described is about evaluating the predictive power of a variable across different subsets of data. 2. Relate to Options This concept is most closely related to **cross-validation**, where data is split into multiple subsets to test the consistency and reliability of a model's predictions.

Explanation

1. Identify the Concept<br /> The concept described is about evaluating the predictive power of a variable across different subsets of data.<br /><br />2. Relate to Options<br /> This concept is most closely related to **cross-validation**, where data is split into multiple subsets to test the consistency and reliability of a model's predictions.
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