Regularization comes into the picture when a model is either overfit or underfit. It is basically used to minimize the error in a dataset. A new piece of information is fit into the dataset to avoid fitting issues.

Regularization in machine learning is a technique used to prevent overfitting and improve the generalization ability of a model. Overfitting occurs when a model learns the training data too well, capturing noise and irrelevant patterns that do not generalize well to unseen data.

There are different types of regularization techniques, including:

L2 Regularization (Ridge Regression): This technique adds a penalty term to the cost function proportional to the square of the magnitude of coefficients. It discourages large coefficients, thus preventing the model from fitting the training data too closely.
L1 Regularization (Lasso Regression): Similar to L2 regularization, L1 regularization adds a penalty term to the cost function, but it’s proportional to the absolute value of the magnitude of coefficients. L1 regularization encourages sparsity in the model by pushing some coefficients to zero, effectively selecting only the most important features.
Elastic Net Regularization: Elastic Net combines both L1 and L2 regularization, providing a balance between the two. It’s useful when there are correlated features in the dataset.
Dropout: Used primarily in neural networks, dropout randomly sets a fraction of the neurons to zero during each training iteration. This helps prevent complex co-adaptations on training data and acts as a form of regularization.
Early Stopping: This technique involves monitoring the performance of the model on a validation set during training and stopping the training process when the performance starts to degrade, thus preventing overfitting.

Regularization helps to control the complexity of a model and makes it more robust by penalizing overly complex models. It encourages models to generalize well to new, unseen data, thereby improving their predictive performance.