Financial Risk Manager Part 1
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What is the main reason the m-fold cross-validation method of model selection is mostly used in modern data science?
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NNikitesh
A
It is relatively easy to execute
B
The method is appropriate in modeling observations that can be used for out-of-sample predictions
C
This method is suitable in large sample sizes
D
All of the above
Explanation:
The m-fold cross-validation method is primarily used because it selects models based on their out-of-sample prediction performance. This method:
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Evaluates model generalization: By partitioning data into training and validation sets multiple times, it tests how well the model performs on unseen data.
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Avoids overfitting: It helps identify models that generalize well rather than just fitting the training data perfectly.
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Selects robust variables: The method tends to choose variables that consistently predict the dependent variable across different data subsets, while excluding variables with small coefficients that fail to perform well in out-of-sample predictions.
While options A and C have some merit (cross-validation is relatively straightforward to implement and works well with large datasets), the primary reason for its widespread use is its ability to assess out-of-sample prediction performance, making option B the most accurate answer.
The text explicitly states: "The m-fold validation method chooses the model that performs the out-of-sample prediction, that is, a model that fits the observations not included in the estimation of the parameters."
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