Applications and Use Cases of Elasticnet
Elastic Net regularization can be useful in various scenarios, including:
- High-dimensional data: In the case of working with large amount of features, and this is one of the main advantages when comparing Elastic Net with Ridge, one can perform features selection when affecting certain coefficients to zero values in order to decrease the model’s complexity and make the results easier to interpret.
- Correlated features: Under circumstance when the dataset has multiple features that are significantly correlated to one another, then Elastic Net can address issues of multicollinearity while still incorporating all the necessary features into the model.
- Sparse solutions: For situations where one wants to encourage sparse solutions (e. g. , for feature selection, or if better interpretability is wanted), Elastic Net can be useful because it has the capability to force coefficients all the way to zero).
- Regression tasks: Elastic Netzis a regression fitting method aimed predominantly at linear regression models where the goal is to find relationships between input features and a continuous target variable.
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