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MMLU / 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?

choices

  1. Bias increase ; Variance increase
  2. Bias increase ; Variance decrease
  3. Bias decrease ; Variance increase
  4. Bias decrease ; Variance decrease

subject

machine_learning

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Source and history

task_id
46ea4a83-12a1-5d39-bdb0-dcf8ecf1e48c
task_key
all--test--46ea4a83-12a1-5d39-bdb0-dcf8ecf1e48c
task_revision_id
1
config / split
all / test

Official source

initial import