How do I tune the parameters for Elastic Net regression?
An example of an AI chatbot conversation about tuning Elastic Net parameters.
How do I tune the parameters for Elastic Net regression?
To tune Elastic Net parameters, you typically adjust the alpha and l1_ratio values. Would you like to know how to choose these values?
Yes, what are the best practices for selecting alpha and l1_ratio?
A common approach is to use cross-validation to evaluate different combinations of alpha and l1_ratio. You can use a grid search for this.
Can you explain what alpha and l1_ratio represent?
Sure! Alpha controls the overall strength of the penalty, while l1_ratio determines the mix between L1 and L2 regularization.
How do I implement grid search in Python for this?
You can use GridSearchCV from sklearn.model_selection. It allows you to specify a range of values for alpha and l1_ratio to find the best combination.
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