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MMLU-Pro / 10426 / You are training a linear regression model for a simple estimation task, and…

Problem

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question

You are training a linear regression model for a simple estimation task, and notice that the model is overfitting to the data. You decide to add in ℓ2\ell_2 regularization to penalize the weights. As you increase the ℓ2\ell_2 regularization coefficient, what will happen to the bias and variance of the model?
Plain-text mathematical notation (without MathML)
You are training a linear regression model for a simple estimation task, and notice that the model is overfitting to the data. You decide to add in ℓ₂ regularization to penalize the weights. As you increase the ℓ₂ regularization coefficient, what will happen to the bias and variance of the model?
Original LaTeX notation
You are training a linear regression model for a simple estimation task, and notice that the model is overfitting to the data. You decide to add in $\ell_2$ regularization to penalize the weights. As you increase the $\ell_2$ regularization coefficient, what will happen to the bias and variance of the model?

category

computer science

options

  1. Bias increase ; Variance increase
  2. Bias decrease ; Variance increase
  3. Neither bias nor variance is affected by $\ell_2$ regularization
  4. Variance remains constant ; Bias decreases
  5. Bias increase ; Variance decrease
  6. Bias remains constant ; Variance increases
  7. Both bias and variance remain constant
  8. Variance remains constant ; Bias increases
  9. Bias decrease ; Variance decrease
  10. Bias remains constant ; Variance decreases

src

ori_mmlu-machine_learning

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Source and history

upstream_id / Original problem ID
10426
task_id
45d8c94b-5b4e-51d7-a4e6-a3f1228f51d3
task_key
default--test--10426
task_revision_id
1
config / split
default / test

Official source

initial import