Significance of Regularization method
Regularization methods combat multicollinearity and overfitting in models. By adding a penalty function, model complexity is constrained, leading to simpler, more generalizable models. L2 regularization is specifically effective at reducing overfitting by penalizing large coefficients, thus promoting a less complex model that performs better on unseen data. This approach enhances the model's ability to generalize beyond the training data.
Synonyms: Stabilization, Constraint, Penalty, Smoothing, Damping, Shrinkage, Regularization technique, Shrinkage method
The below excerpts are indicatory and do represent direct quotations or translations. It is your responsibility to fact check each reference.
The concept of Regularization method in scientific sources
Regularization is a technique that addresses multicollinearity and overfitting. It constrains model complexity by adding a penalty function, leading to simpler, more generalizable models, like with L2 regularization.
From: Sustainability Journal (MDPI)
(1) Techniques used to improve the conditioning of equations and recognition accuracy by adding penalty terms.[1] (2) The "regularization methods" permit the algorithm not to overfit the data and reduce the variance of the model, allowing the neural network to generalise the dynamic behaviour of the train.[2] (3) Are supported by LightGBM, such as L 1 regularization and L 2 regularization, preventing overfitting in the model.[3] (4) Regularization methods like L 2 regularization effectively mitigate overfitting by penalizing model complexity and facilitating the development of a simpler model.[4] (5) A technique used to solve multicollinearity problems by adding a penalty function to constrain the complexity of the model.[5]