Predicting the magnitude of risk for non-curative endoscopic submucosal dissection in superficial esophageal cancer using explainable artificial intelligence

Figure 2 Least absolute shrinkage and selection operator regression analysis for feature selection. A: Coefficient paths of 30 variables vs log(λ). Vertical lines indicate key λ values: 0.023 (9 variables, minimal mean squared error) and 0.058 (7 core variables under 1-SE rule); B: Cross-validation curve shows deviance vs log(λ) with error bands. λ = 0.023 gives minimum deviance; λ = 0.058 provides optimal parsimony. Together, these demonstrate regularization’s control of model complexity and prediction performance.