- What is the main goal of feature selection in machine learning?
A) To increase the number of features
B) To reduce the number of features
C) To increase model complexity
D) To add noise to the data
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Answer: B) To reduce the number of features
- Which feature selection method involves adding features one by one and evaluating the model performance at each step?
A) Backward Elimination
B) Recursive Feature Elimination
C) Forward Selection
D) Random Forest
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Answer: C) Forward Selection
True/False Questions
True or False: Feature selection is unnecessary if all features are relevant.
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Answer: False (Even if all features are relevant, reducing the number of features can still help simplify the model and improve computational efficiency.)
True or False: Feature selection always leads to better model performance.
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Answer: False (While feature selection can improve performance by reducing overfitting, it does not always guarantee better performance.)
True or False: High correlation between features is a reason to perform feature selection.
Show answer
Answer: True (High correlation between features can lead to multicollinearity, which can affect model performance.)
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