Description: Regularization techniques refer to a set of methods used in machine learning and statistics to prevent model overfitting. Overfitting occurs when a model fits too closely to the training data, capturing noise and irrelevant patterns, resulting in poor performance on unseen data. Regularization introduces constraints or penalties to the model, helping to maintain its simplicity and generalization. There are various regularization techniques, such as L1 (Lasso) and L2 (Ridge), which add penalty terms to the model’s loss function. These techniques not only improve generalization capability but can also help identify relevant features by reducing model complexity. Regularization is essential in contexts where data is limited or noisy, as it allows for the construction of more robust and reliable models. In summary, regularization techniques are a crucial tool in a data scientist’s toolbox, as they help balance model accuracy with its ability to generalize to new data.
