Ridge regression, also known as L2 Regularization, is a regression technique that introduces a small amount of bias to reduce overfitting. It does this by minimizing the sum of squared residualsplusa penalty, where the penalty is equal to lambda times the slope squared. Lambda refers to the se...
GLM: Linear/Logistic Regression with L1 ∨ L2 Regularization GAM: Generalized Additive Models using B-splines Tree: Decision Tree for Classification and Regression FIGS: Fast Interpretable Greedy-Tree Sums (Tan, et al. 2022) XGB1: Extreme Gradient Boosted Trees of Depth 1, with optimal binning ...
This simple model shows interesting behavior, elucidating the role of regularization and under- vs. over-parameterization in learning machines. First we consider the interpolation limit (λ = 0, Fig. 3a). The generalization error simplifies to \({E}_{g}=(1-\alpha ){{\Theta }}(1-\...
Subword regularization: Improving neural network translation models with multiple subword contextualized word representations. arXiv preprint arXiv:1802.05365, 2018. candidates. arXiv preprint arXiv:1804.10959, 2018. Post, M. A call for clarity in reporting bleu scores. arXiv preprint arXiv:1804.08771,...
For the SVM we adopted a linear kernel (to obtain weights that can be easily interpreted), with a regularization parameter \(\in \{0.1, 0.5, 1, 5, 10, 20, 50\}\). We also tune the class weight assigned to the low and high risk categories; for this parameter, the following ...