What outcome is expected from using regularization in machine learning?

The expected outcome from using regularization in machine learning is to decrease model complexity and prevent overfitting. Regularization techniques, such as L1 (Lasso) and L2 (Ridge) regularization, add a penalty term to the loss function during the training of a model. This penalty discourages overly complex models which can fit the training data too closely, capturing noise as well as the underlying patterns. By applying this penalty, the model is encouraged to maintain simpler patterns, which often results in better generalization to unseen data.

In scenarios where a model is too complex, it may perform exceptionally on the training dataset but poorly on validation and test datasets, a situation known as overfitting. Regularization aims to strike a balance between fitting the training data well and keeping the model general enough to work effectively on new data.

Increasing the training dataset size contributes to better model performance but is not the direct purpose of regularization. Enhancing computational power during training describes improvements in hardware or algorithm efficiency and does not relate to the concept of regularization. Eliminating all noise from the data is overly ambitious and unrealistic; regularization does not aim to remove noise but instead to manage its influence on model predictions.