What technique can be used to improve the performance of a machine learning model?

Regularization is a powerful technique employed to enhance the performance of machine learning models by addressing issues related to overfitting. Overfitting occurs when a model learns the noise and details in the training data to the extent that it negatively impacts its performance on new, unseen data. This often happens when the model is too complex, capturing relationships that do not generalize well beyond the training dataset.

Regularization works by adding a penalty to the loss function based on the complexity of the model. Two common forms of regularization are L1 (Lasso) and L2 (Ridge) regularization. These methods discourage models from fitting overly complex patterns by shrinking the coefficients of the features, effectively simplifying the model. This simplification helps maintain the model’s ability to generalize, leading to improved accuracy on validation or test datasets.

Utilizing regularization can significantly enhance the robustness of the model, making it more reliable and accurate when making predictions with new data. This added layer of stability is particularly crucial in scenarios where data might be noisy or where the risk of overfitting is high.

In contrast, adding more hidden layers or using irrelevant data typically complicates the model or introduces noise, neither of which tend to improve performance. Using smaller datasets usually