What is Elasticnet in Sklearn?
What is the difference between Lasso, Ridge, and Elastic Net?
Lasso deploys L1 regularization, which can make the coefficients possibly equal to zero, thus allowing feature selection. Ridge employs L2 regularization, which unequals coefficients towards no, although it does not set it to zero completely. Elastic Net is a more effective method because it uses both the L1 and L2 forms of regularization, but consolidated into one.
When should I use Elastic Net over Lasso or Ridge?
Elastic Net can be particularly useful when you have a large number of features, some of which may be correlated, and you want to perform feature selection while also handling multicollinearity.
How do I choose the values for alpha and l1_ratio in Elastic Net?
The values for alpha (regularization strength) and l1_ratio (mixing parameter) can be chosen through techniques like cross-validation or grid search, evaluating the model’s performance on a validation set or using a scoring metric like mean squared error (for regression tasks).
What is Elasticnet in Sklearn?
To minimize overfitting, in machine learning, regularizations techniques are applied which helps to enhance the model’s generalization performance. ElasticNet is a regularized regression method in scikit-learn that combines the penalties of both Lasso (L1) and Ridge (L2) regression methods.
This combination allows ElasticNet to handle scenarios where there are multiple correlated features, providing a balance between the sparsity of Lasso and the regularization of Ridge. In this article we will implement and understand the concept of Elasticnet in Sklearn.
Table of Content
- Understanding Elastic Net Regularization
- Implementing Elasticnet in Scikit-Learn
- Hyperparameter Tuning with Grid Search Elastic Net
- Applications and Use Cases of Elasticnet