Which strategy is typically used to improve model accuracy by reducing noise?

Regularization is a strategy used to improve model accuracy by reducing noise and preventing overfitting during the training of a machine learning model. It works by adding a penalty term to the loss function that the model is trying to minimize, which discourages complex models that fit the training data too closely. By controlling the complexity of the model, regularization helps maintain a balance between fitting the data well and generalizing effectively to unseen data, thus improving overall predictive performance.

Cross-validation is a technique used to assess the performance of a model by training it on different subsets of the data and validating it on other subsets, but it does not directly reduce noise within the model itself. Standardization is a preprocessing step that involves scaling the features to have a mean of zero and a standard deviation of one, improving the model's convergence speed but not reducing noise in the data. Tokenization refers to breaking down text into smaller units, such as words or subwords, which is essential in natural language processing but does not directly address model accuracy in the context of noise reduction.

In summary, regularization specifically targets the issue of noise and complexity in models, making it the most relevant choice for improving model accuracy through reducing noise.