Which methods can be used to avoid overfitting?

Using regularization techniques is a highly effective way to combat overfitting in machine learning models. Overfitting occurs when a model learns the training data too well, including noise and outliers, which negatively impacts its performance on unseen data. Regularization introduces additional constraints or penalties to the model, which can prevent it from becoming too complex.

One common form of regularization is L1 (Lasso) and L2 (Ridge) regularization. These techniques add a term to the loss function that penalizes large weights. By doing so, the model is encouraged to keep weights small, promoting simpler models that generalize better to new data. Consequently, the regularized model is less likely to fit the noise in the training dataset, resulting in enhanced performance on validation and test datasets.

Utilizing regularization techniques is essential, especially in scenarios where training data is limited or when dealing with particularly complex models that may have a tendency to fit the training data too closely. This strategy ensures a better balance between bias and variance, leading to more robust model performance.