Explain how the ElasticNet regression combines the properties of Ridge and Lasso regression.
- By alternating between L1 and L2 regularization
- By using a weighted average of L1 and L2
- By using both L1 and L2 regularization
- By using neither L1 nor L2 regularization
ElasticNet regression combines the properties of Ridge and Lasso by using both L1 and L2 regularization. This hybrid approach combines Lasso's ability to perform feature selection with Ridge's ability to handle multicollinearity, providing a balance that can be fine-tuned using hyperparameters.
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