Which process helps to avoid overfitting in machine learning?

Implementing regularization techniques effectively helps to avoid overfitting in machine learning models. Overfitting occurs when a model learns not just the underlying patterns in the training data but also the noise, leading to poor performance on unseen data. Regularization methods, such as L1 (Lasso) and L2 (Ridge) regularization, add a penalty term to the loss function. This penalty discourages overly complex models by shrinking the coefficients of less important features towards zero, thus simplifying the model. By balancing the trade-off between fitting the training data well and keeping the model sufficiently simple, regularization enhances the model's generalization ability.

In contrast, increasing the number of features can exacerbate overfitting, as more features can lead to capturing noise present in the training set. Using a higher learning rate might lead to instability in training and convergence issues, rather than preventing overfitting. Similarly, decreasing the dataset size typically makes overfitting more likely, as the model may latch onto too few examples without learning the true underlying distribution. Thus, implementing regularization techniques stands out as the most effective way to prevent overfitting.