Regularization
What is regularization in AI?
Regularization in AI is a technique used to prevent overfitting, which occurs when a model learns the training data too well and performs poorly on unseen data. Regularization adds a penalty to the loss function, discouraging the model from learning overly complex patterns in the training data that may not generalize well.
How does regularization work?
Regularization works by adding a term to the loss function that penalizes large weights in the model. This encourages the model to learn simpler, more generalizable patterns in the data. The strength of the regularization is controlled by a hyperparameter, which can be tuned to balance the trade-off between fitting the training data and preventing overfitting.
There are several types of regularization, including L1 and L2 regularization, which penalize the absolute and squared values of the weights, respectively.
What is the role of regularization in training a model?
Regularization plays a crucial role in training a model. It helps to control the complexity of the model, preventing it from learning overly complex patterns in the training data that may not generalize well. This can improve the model's performance on unseen data, making it more useful in practice.
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