How can I improve the performance of my regression model?
There are several strategies you can use, such as feature selection, regularization, and hyperparameter tuning. Which one would you like to explore first?
Can you explain feature selection?
Feature selection involves choosing the most relevant features for your model to improve accuracy and reduce overfitting. Techniques include backward elimination, forward selection, and using algorithms like LASSO.
What about regularization?
Regularization adds a penalty to the loss function to prevent overfitting. Common methods include L1 (LASSO) and L2 (Ridge) regularization. Would you like to know how to implement these?
Yes, please!
To implement LASSO in Python, you can use the Lasso
class from the sklearn.linear_model
module. Would you like a code example?
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