Elimination of non-informative attributes to simplify a dataset is known as:

• A. Attribute reduction
• B. Row reduction
• C. Feature extraction
• D. Normalization

Answer: (A)

Removing non-informative attributes to simplify a dataset is attribute reduction. This means keeping only the attributes that contribute to predicting the target and discarding those that don’t, which reduces dimensionality and can improve model performance by reducing noise and computation.

Row reduction would mean dropping entire records (rows) rather than attributes. Feature extraction transforms the data into new features, often creating combinations or reduced representations, rather than simply removing uninformative attributes. Normalization scales values but does not remove attributes.