Which 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
Which 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 statement that is true 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 not necessarily reduce or eliminate overfitting. In fact, a smaller k might lead to a higher risk of overfitting because the training set is smaller.
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A high variance of parameter estimates across cross-validation subsamples does indicate likely overfitting. This is because overfitting is characterized by a model that is too sensitive to the training data, leading to high variability in the estimates when the model is applied to different subsamples.
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A low variance of parameter estimates across cross-validation subsamples does not necessarily indicate overfitting. In fact, it might suggest that the model is not overfitting because the estimates are stable across different subsamples.
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Cross-validation with a large k will not necessarily reduce or eliminate overfitting. While a larger k does mean a larger training set, which can help prevent overfitting, it also means a smaller validation set, which can lead to a higher variance in the validation error estimate.
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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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