Fig. 1From: Clinical characteristic and pathogenesis of tumor-induced acute pancreatitis: a predictive modelSelection of risk factors of tumor-induced AP using the LASSO logistic regression algorithm. A LASSO coefficient profiles of the 15 candidate variables. Vertical line was plotted at the given lambda, selected by tenfold cross-validation with minimum classification error and minimum classification error plus 1 standard error, respectively. For the optimal lambda that gives minimum classification error plus 1 standard error, 6 features with a non-0 coefficient were selected. B Penalization coefficient lambda in the LASSO model was tuned using tenfold cross-validation and the “lambda.1se” criterion. Area under the curve (AUC) metrics (y-axis) were plotted against log (lambda) (bottom x-axis). Top x-axis indicates the number of predictors for the given log (lambda). Red dots indicate average AUC for each model at the given lambda, and vertical bars through the red dots show the upper and lower values of the AUC according to the 10-fold cross-validation. Vertical black lines define the optimal lambda that gives the minimum classification error plus 1 standard errorBack to article page