Question 4Which of the following statements about a high-complexity model in a linear regression setting is TRUE?1 pointCross-validation with a small k will reduce or eliminate overfitting.A high variance of parameter estimates across cross-validation subsamples indicates likely overfitting.A low variance of parameter estimates across cross-validation subsamples indicates likely overfitting.Cross-validation with a large k will reduce or eliminate overfitting.
Question
Question 4Which of the following statements about a high-complexity model in a linear regression setting is TRUE?1 pointCross-validation with a small k will reduce or eliminate overfitting.A high variance of parameter estimates across cross-validation subsamples indicates likely overfitting.A low variance of parameter estimates across cross-validation subsamples indicates likely overfitting.Cross-validation with a large k will reduce or eliminate overfitting.
Solution
The correct statement about a high-complexity model in a linear regression setting is: "A high variance of parameter estimates across cross-validation subsamples indicates likely overfitting."
Here's why:
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Cross-validation with a small k will reduce or eliminate overfitting: This is not true. A smaller k in k-fold cross-validation means fewer subsets of the data are used for testing, and the model is trained on a larger portion of the data. This can actually lead to overfitting, as the model may become too tailored to the training data and perform poorly on unseen data.
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A high variance of parameter estimates across cross-validation subsamples indicates likely overfitting: This is true. If the parameter estimates vary greatly across different subsamples, it suggests that the model is too complex and is overfitting to the specific quirks of each subsample.
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A low variance of parameter estimates across cross-validation subsamples indicates likely overfitting: This is not true. Low variance in parameter estimates suggests that the model is stable and generalizes well across different subsamples.
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Cross-validation with a large k will reduce or eliminate overfitting: This is not necessarily true. A larger k in k-fold cross-validation means more subsets of the data are used for testing, and the model is trained on a smaller portion of the data. While this can help to prevent overfitting, it doesn't necessarily eliminate it. Overfitting is more related to the complexity of the model and the amount of noise in the data.
Similar Questions
Question 3If a low-complexity model is underfitting during estimation, which of the following is MOST LIKELY true (holding the model constant) about K-fold cross-validation?1 pointK-fold cross-validation will still lead to underfitting, for any k.K-cross-validation with a small k will reduce or eliminate underfitting.K-fold cross-validation with a large k will reduce or eliminate underfitting.None of the above.
Which of the following statements about model complexity is TRUE? 1 pointHigher model complexity leads to a lower chance of overfitting.Higher model complexity leads to a higher chance of overfitting. Reducing the number of features while adding feature interactions leads to a lower chance of overfitting.Reducing the number of features while adding feature interactions leads to a higher chance of overfitting.
Question 1In K-fold cross-validation, how will increasing k affect the variance (across subsamples) of estimated model parameters?1 pointIncreasing k will not affect the variance of estimated parameters. Increasing k will usually reduce the variance of estimated parameters. Increasing k will usually increase the variance of estimated parameters. Increasing k will increase the variance of estimated parameters if models are underfit, but reduce it if models are overfit.
What is the consequence of a model having low bias and high variance? Overfitting Underfitting High generalization Low computational complexity
Which of the following statements about cross-validation is/are True?1 pointCross-validation is essential step in hyperparameter tuning.We can manually generate folds by using KFold function.GridSearchCV is commontly used in cross-validation.All of the above are True.
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