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Effect of Regularization on Error Tradeoffs in a Forecasting Model
Case context: A data scientist builds a model that predicts daily electricity demand. The model overfits the training set, so they add stronger regularization using weight decay, a sparsity penalty, or dropout.
Question: What effect should this change have on variance and bias?
Sample answer: Regularization should lower the model's variance, but it will usually raise its bias.
Key points:
- Regularization decreases variance.
- Regularization increases bias.
- Examples here include weight decay, a sparsity penalty, and dropout.
Rubric: The student must identify that adding regularization reduces variance and increases bias.
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Related
How does regularization change the balance between training fit and generalization?
True or False: L2 regularization or dropout often lowers variance, even though it can make the model slightly more biased.
Adding regularization such as L2, L1, or dropout usually reduces _____ while increasing bias.
What happens to bias and variance when regularization is increased?
True or False: Dropout is a regularization method that often lowers variance while making the model slightly more biased.
Adding regularization lowers _____ in a machine learning model, but it can also raise bias.
Match each regularization method to the mechanism it uses to reduce overfitting.
Order the actions for deciding whether regularization is the right fix for a high-variance model.
Which three techniques are commonly used to regularize a model and often reduce variance at the cost of some added bias?
True or False: L1 Regularization Cannot Reduce Variance
L1 regularization, L2 regularization, and _____ are common techniques for reducing overfitting.
Match each regularization idea to the effect it has on model error.
Arrange the stages that show how regularization changes the fit of a model.
How Regularization Changes Variance and Bias
Effect of Regularization on Error Tradeoffs in a Forecasting Model
How Regularization Usually Changes Bias and Variance