What is the difference between Feature Selection and Feature Extraction?
Feature selection is the process of choosing precise features, from a features pool. This helps in simplification, regularization and shortening training time. This can be done with various techniques: e.g. Linear Regression, Decision Trees.
Feature extraction is the process of converting the raw data into some other data type, with which the algorithm works is called Feature Extraction. Feature extraction creates a new, smaller set of features that captures most of the useful information in the data.
The main difference between them is Feature selection keeps a subset of the original features while feature extraction creates new ones.