Fig. 4
From: To tune or not to tune, a case study of ridge logistic regression in small or sparse datasets
Nested loop plot of root mean squared error (RMSE) of predictions multiplied by the square root of sample size N by the number of predictors K ∈ {2, 5, 10}, noise absent or present, the sample size N ∈ {100, 250, 500} and the effect multiplier a ∈ {0.5, 1} for simulated scenarios with expected value of Y, E(Y) = 0.1. Further results are contained in Table S6 and S7. OP, prediction oracle; D, deviance; GCV, generalized cross-validation; CE, classification error; RCV50, repeated 10-fold cross-validated deviance with θ = 0.5; RCV95, repeated 10-fold cross-validated deviance with θ = 0.95; AIC, Akaike’s information criterion; IP, shrinkage based on informative priors; WP, shrinkage based on weakly informative priors; FLIC, Firth’s logistic regression with intercept-correction