Feature extraction is the process of transforming raw data into numerical features that can be processed while preserving the information in the original data set. In machine learning, feature extraction is an essential step in preparing data for analysis and modeling. The goal of feature extraction is to identify the most important characteristics or properties of the data that can be used to make accurate predictions or classifications.

Feature extraction can be accomplished manually or automatically. Manual feature extraction requires identifying and describing the features that are relevant for a given problem and implementing a way to extract those features. Automated feature extraction uses specialized algorithms or deep networks to extract features automatically from signals or images without the need for human intervention.

Feature extraction is a subset of feature engineering, which also includes feature selection and feature combination. Feature selection is the process of choosing which features are most likely to enhance the quality of the prediction variable or output. Feature combination involves creating new features by combining existing ones.

Deep learning algorithms can process large raw datasets without having to resort to feature engineering. However, its important to note that deep learning algorithms still require careful preprocessing and cleaning of the input data.

In summary, feature extraction is a critical step in machine learning that involves transforming raw data into numerical features that can be processed while preserving the information in the original data set. It can be accomplished manually or automatically and is a subset of feature engineering, which also includes feature selection and feature combination.