AI Terms
Definition: Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function.
Detailed Description:
Regularization helps to avoid overfitting by discouraging the model from learning overly complex patterns in the training data. Common regularization techniques include:
Regularization can improve the generalization performance of a model, making it less likely to overfit the training data and perform poorly on new, unseen data.
Related Terms:
FAQs:
What is the difference between L1 and L2 regularization?
How does regularization help prevent overfitting?
When should regularization be used?
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