The correct answer is C. feature extraction.
PCA is a dimensionality reduction technique that projects data points onto a lower-dimensional space while preserving as much of the variance as possible. This can be useful for visualization, data compression, and machine learning.
Forward feature selection is a method of feature selection that starts with all features and then iteratively removes features that do not improve the model’s performance. Backward feature selection is a method of feature selection that starts with no features and then iteratively adds features that improve the model’s performance.
Neither forward feature selection nor backward feature selection are the same as PCA. PCA is a method of feature extraction, not feature selection. Feature extraction is a process of transforming the original features into a new set of features that are more useful for the task at hand. PCA is a type of feature extraction that projects data points onto a lower-dimensional space while preserving as much of the variance as possible.