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What is the purpose of using regularization in model selection?
Practice Questions
Q1
What is the purpose of using regularization in model selection?
To increase model complexity
To prevent overfitting
To improve feature selection
To enhance data preprocessing
Questions & Step-by-Step Solutions
What is the purpose of using regularization in model selection?
Steps
Concepts
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.
No concepts available.
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