What does feature selection aim to achieve?

Feature selection aims to identify and select a subset of relevant features from the larger set of available features in a dataset. This process is crucial because having too many features can lead to various issues, such as overfitting, increased computational costs, and longer training times for models. By selecting only the most important features, feature selection helps improve model accuracy, efficiency, and interpretability.

When narrowing down features, techniques used may include statistical tests, algorithms, or domain knowledge to determine which features have the strongest relationship with the target variable. Thus, effective feature selection enhances model performance by focusing on the components of the dataset that contribute the most valuable information.