Final answer:
Regularization in machine learning is a technique used to prevent overfitting and improve the performance of a model. It involves adding a penalty term to the model's objective function to reduce the complexity of the model. This promotes generalization.
Step-by-step explanation:
Regularization in machine learning is a technique used to prevent overfitting and improve the performance of a model. It involves adding a penalty term to the model's objective function to reduce the complexity of the model. This penalty term discourages the model from relying heavily on any one feature or variable, thereby promoting generalization.
For example, in linear regression, regularization can be achieved by adding a regularization term to the sum of squared errors. This term penalizes the model for having large coefficients and helps control model complexity, making it less likely to overfit the training data.
Regularization can be particularly useful when dealing with high-dimensional datasets or when we have limited training data. By balancing the trade-off between model complexity and the fit to the training data, regularization helps prevent the model from memorizing the training data and improves its ability to generalize to unseen data.