Which method is frequently used to prevent overfitting?

The method frequently used to prevent overfitting in machine learning is regularization. Regularization techniques add a penalty term to the model’s loss function, discouraging overly complex models that fit the training data too closely. There are different types of regularization methods, such as L1 regularization (Lasso), L2 regularization (Ridge), and elastic net regularization, each with its own way of penalizing overly complex models. Regularization helps in controlling the model’s complexity, thereby reducing overfitting and improving its generalization ability to unseen data.