What is the purpose of using regularization in model selection?

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Question: What is the purpose of using regularization in model selection?

Options:

Correct Answer: To prevent overfitting

Solution:

Regularization is used to prevent overfitting by adding a penalty for larger coefficients in the model.

What is the purpose of using regularization in model selection?

What is the purpose of using regularization in model selection?

Step 1: Understand that when we create a model, we want it to learn from the data.
Step 2: Realize that sometimes a model can learn too much from the training data, which is called overfitting.
Step 3: Overfitting means the model performs well on training data but poorly on new, unseen data.
Step 4: Regularization is a technique used to help the model generalize better to new data.
Step 5: It does this by adding a penalty for having large coefficients (weights) in the model.
Step 6: By keeping the coefficients smaller, the model becomes simpler and less likely to overfit.

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