What is the difference between feature selection and feature extraction in machine learning?

Feature selection refers to the process of selecting a subset of relevant features from the original dataset, effectively reducing dimensionality and improving model performance by retaining only the most important variables. In contrast, feature extraction involves transforming the original features into a new set of features, typically through techniques like Principal Component Analysis (PCA), where the new features capture the essential information of the data while reducing dimensionality. Both approaches aim to optimize model performance but do so through different methodologies.