Can dimensionality reduction be used to improve the performance of machine learning models? If so, how?
- All of the above
- By improving computational efficiency
- By reducing overfitting
- By simplifying the model
Dimensionality reduction can improve the performance of machine learning models by reducing overfitting (as the model becomes less complex), simplifying the model (making it easier to interpret), and improving computational efficiency (reducing training time and resource requirements).
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