Why is feature selection important in machine learning?

Feature selection is a critical process in machine learning that involves identifying and selecting a subset of relevant features for use in model construction. This practice is essential for several reasons, particularly in reducing overfitting and improving interpretability.

When a model is trained on a large number of features, especially those that are irrelevant or redundant, it risks capturing noise in the data rather than the underlying patterns. This can lead to overfitting, where the model performs well on training data but poorly on unseen data, failing to generalize effectively. By selecting only the most pertinent features, practitioners can mitigate this risk and develop models that are more robust and perform better on new data.

Additionally, reducing the number of features enhances the interpretability of the model. Simplified models with fewer features are easier for practitioners and stakeholders to understand, making it clear how each feature contributes to the predictions. This clarity is crucial, especially in domains where understanding model decisions is important for compliance, ethics, or actionable insights.

In contrast, increasing the size of the dataset is not a direct benefit of feature selection, and more features do not always contribute positively to model performance. Thus, focusing on the most impactful features is fundamental to building effective machine learning models.