Feature extraction involves reducing the amount of data by identifying the most important attributes, or features, needed for analysis. For instance, in image recognition, the system identifies edges or textures as features to simplify and speed up the image processing task. This process enhances the predictive power of models while reducing computational cost, drawing parallels to dimensionality reduction techniques like Principal Component Analysis (PCA).