The correct answer is A. To prevent overfitting by adding a penalty term to the loss function.
Regularization is a technique used in machine learning to prevent overfitting. Overfitting occurs when a model learns the training data too well and is unable to generalize to new data. Regularization works by adding a penalty term to the loss function that discourages the model from becoming too complex. This penalty term can be either an L1 penalty or an L2 penalty.
The L1 penalty is also known as the Lasso penalty. It penalizes the model for having large coefficients. This encourages the model to use fewer features and to focus on the most important features.
The L2 penalty is also known as the Ridge penalty. It penalizes the model for having large squared coefficients. This encourages the model to use all of the features, but to give smaller weights to the less important features.
Regularization can be used with any type of machine learning model, but it is most commonly used with linear models and support vector machines.
Option B is incorrect because regularization does not remove outliers from the data. Outliers are data points that are very different from the rest of the data. They can be caused by errors in the data collection process or by genuine variation in the data. Outliers can make it difficult for a model to learn the underlying relationship between the features and the target variable. There are a number of techniques that can be used to deal with outliers, such as removing them from the data or using robust regression.
Option C is incorrect because regularization does not increase model complexity. In fact, regularization can actually decrease model complexity by encouraging the model to use fewer features.
Option D is incorrect because regularization does not reduce dimensionality. Dimensionality reduction is a technique that is used to reduce the number of features in a dataset. This can be done by using a technique such as principal component analysis or t-distributed stochastic neighbor embedding.