Fig. 2.

Intra- and inter-data set feature reduction using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. In the upper panel, tuning parameter (lambda, λ) selection in the LASSO model used 10-fold cross validation via minimum criteria. Dotted vertical lines were drawn at the optimal values by using the minimum criteria (the value 32 means that the 117 features reduction to 32 features). In the lower panel, tuning parameter (lambda, λ) selection in the LASSO model used 10-fold cross validation via minimum criteria. Dotted vertical lines were drawn at the optimal values by using the minimum criteria (the value 43 means that the 117 features reduction to 43 features).