What is a benefit of using feature selection in model training?

Feature selection is a vital process in machine learning that involves identifying and selecting a subset of relevant features for model training. One of the primary benefits of using feature selection is that it improves interpretability and reduces overfitting.

By selecting only the most important features, the model becomes simpler and easier to understand. This simplification helps practitioners and stakeholders interpret how the model makes decisions based on the chosen features, enhancing transparency in model outcomes. Additionally, a model trained with fewer, more relevant features can generalize better to unseen data, thus reducing the risk of overfitting. Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, potentially leading to poor performance on new datasets.

Incorporating feature selection fosters a more robust model by eliminating irrelevant or redundant features that could mislead the learning process or add unnecessary complexity. Therefore, it leads to more reliable predictions and can enhance the model's overall performance when applied to new data.