What is the main purpose of regularization techniques in machine learning?

The primary purpose of regularization techniques in machine learning is to prevent overfitting. Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise and fluctuations that do not generalize to unseen data. This often results in a model that performs very well on the training dataset but poorly on new, unseen datasets.

Regularization methods, such as L1 (Lasso) and L2 (Ridge) regularization, add a penalty term to the loss function used in training the model. This penalty discourages the model from fitting the training data too closely by shrinking the coefficients of less important features towards zero or by preventing their values from becoming excessively large. Consequently, this leads to a model that is more generalizable and performs better on new data, thereby combating overfitting effectively.

Other options relate to important aspects of machine learning but do not directly address the specific function of regularization. Enhancing model interpretability, for example, is focused on understanding how different features influence predictions, which is separate from the ability to generalize. Similarly, increasing training speed addresses the efficiency of the training process rather than model generalization capabilities, and improving data collection methods pertains to the quality and quantity of the data being