shrinkagebased
Shrinkagebased is a term that can refer to several concepts depending on the context, but it most commonly relates to techniques used in statistical modeling and machine learning to prevent overfitting. Overfitting occurs when a model learns the training data too well, including its noise and outliers, leading to poor performance on new, unseen data.
In statistical modeling, shrinkage refers to methods that introduce a degree of bias into the model's estimates
Shrinkage-based methods are valuable when dealing with datasets that have a large number of features, especially