Feature extraction is a crucial process in machine learning and pattern recognition that involves transforming raw data into a set of usable features. This technique aims to reduce the dimensionality of the data while retaining its essential information, making it easier to analyze and interpret. By creating new features from existing ones, feature extraction helps summarize the data effectively, enhancing model performance and accuracy. It is particularly useful when dealing with large datasets, as it simplifies the complexity and reduces computational resource requirements. Common methods for feature extraction include factor analysis and principal component analysis.