Regularization
is a machine learning technique used to reduce overfitting by adding constraints or penalties to a model during training, encouraging it to learn patterns that generalize better to unseen data.
What is Regularization?
Regularization modifies the model's training objective to discourage overly complex models or excessively large parameter values. Common methods include L1 regularization, which can encourage some parameters to become zero, and L2 regularization, which penalizes large parameter values.
Why is Regularization Important?
Regularization helps improve a model's ability to generalize beyond its training data. It can reduce sensitivity to noise and prevent a model from relying too heavily on individual features or memorizing training examples.
Common use cases
Regularization is commonly used in linear regression, logistic regression, neural networks, deep learning, and other machine learning models where overfitting is a concern.