Regularization’s purpose lies in mitigating overfitting tendencies and enhancing the overall capacity of models to generalize accurately. Overfitting occurs when a model learns to fit the training data too closely, capturing noise and irrelevant patterns that may not generalize well to new, unseen data. Regularization methods introduce constraints or penalties to the model’s training process, discouraging it from becoming overly complex and thus enhancing its ability to perform on unseen examples.
The essence of regularization lies in finding a balance between fitting the training data closely and avoiding excessive complexity. One common form of regularization is L2 regularization (also known as ridge regularization), which adds a penalty term to the model’s loss function based on the magnitude of the model’s coefficients. This encourages the model to prioritize smaller coefficient values, effectively reducing its tendency to overfit.
Regularization techniques like dropout and early stopping are commonly used in neural networks to prevent overfitting. Dropout randomly deactivates a portion of neurons during training, forcing the network to learn more robust features and reducing reliance on specific neurons. Early stopping monitors the model’s performance on a validation set and halts training when performance starts deteriorating, preventing the model from over-optimizing on the training data.
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