What process can enhance a model's performance by reducing noise in the data?

Data cleansing is the process that significantly enhances a model's performance by addressing and reducing noise within the data. Noise in data can originate from various sources, such as errors in data entry, incorrect values, or irrelevant information that does not contribute to the model's predictive capabilities. By systematically identifying and correcting these inaccuracies, data cleansing helps to create a cleaner dataset, which allows the model to learn more effectively.

The importance of data cleansing lies in its ability to improve the quality and reliability of the data being fed into the model. When data is clean and relevant, the training process becomes more efficient, and the model is less likely to misinterpret patterns or relationships due to the presence of noisy or faulty data. Ultimately, this leads to better predictive performance, as the model can focus on genuine signals rather than being distracted by misleading or erroneous information.

In contrast, the other methods, while beneficial in their own ways, do not specifically target the reduction of noise in the data. Feature extraction focuses on identifying and selecting the most informative variables, data augmentation aims to increase the diversity of the training dataset, and feature reduction (or dimensionality reduction) seeks to reduce the number of features but does not inherently clean the data. Each of these techniques has its own merits,