How does Principal Component Analysis (PCA) assist in data preprocessing?
- It increases data complexity by adding more features
- It reduces dimensionality by transforming variables into a new set of uncorrelated variables, known as principal components
- It removes outliers from the dataset
- It standardizes the data by scaling it to a specific range
PCA assists in data preprocessing by reducing dimensionality. It transforms the original variables into a new set of uncorrelated variables, known as principal components, preserving essential information while reducing computational complexity.
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