Why is feature selection important in machine learning?

Feature selection is a crucial step in the machine learning process because it simplifies data analysis and improves accuracy. By selecting only the most relevant features from the dataset, the model becomes less complex and more efficient, which often leads to better performance. With fewer features, the model can focus on the most informative data, potentially reducing overfitting, where a model learns noise rather than the underlying pattern in the data.

Moreover, simplifying the model by reducing the number of features not only helps in enhancing the model's interpretability but also speeds up the training process. This is particularly important when dealing with large datasets, where having too many features can lead to increased computation time and resource consumption.

In contrast, while identifying redundant data is beneficial, the primary emphasis of feature selection is more about retaining relevant information rather than just identifying what to discard. Additionally, while domain expertise can aid in understanding which features are important, it is not a strict requirement for implementing feature selection, as there are automated techniques that can accomplish this without deep domain knowledge.