Elastic-Net-Regression
You can access the code from HERE:
elastic-net-regression
It is a combination of Ridge and lasso Combination.
Elastic net regression is a type of regularized linear regression that combines the L1 regularization of lasso regression with the L2 regularization of ridge regression. In regularized linear regression, a penalty term is added to the objective function that the model is trying to minimize. The purpose of this penalty term is to prevent overfitting, which occurs when a model fits the training data too well and does not generalize well to new data.
The objective function for elastic net regression is:
Loss = (1/n) * ∑(yi — ŷi)² + λ * (r * ∑|bi| + (1-r) * ∑bi²)
where n is the number of observations, yi is the true value for the i-th observation, ŷi is the predicted value for the i-th observation, bi is the coefficient for the i-th feature, λ is the regularization parameter, and r is the mixing parameter. The regularization parameter determines the strength of the penalty term and can be adjusted to control the model’s complexity. The mixing parameter determines the relative strength of the L1 and L2 penalty terms and can take values between 0 and 1.
The objective function for elastic net regression is a combination of the objective functions for lasso regression and ridge regression. The L1 penalty term helps to constrain the coefficients of the model and can result in some of the coefficients being set to zero, while the L2 penalty term helps to constrain the coefficients and prevent them from becoming too large. Elastic net regression is particularly useful when there are many features in the dataset, as it can help to reduce the complexity of the model and improve the generalization of the model to new data.
Elastic net regression is typically used in situations where the goal is to build a predictive model that is both accurate and interpretable. It can be used in a variety of applications, including predicting stock prices, modeling the relationship between temperature and atmospheric pressure, and predicting the outcomes of sporting events.
