Fig. 2 | Scientific Reports

Fig. 2

From: Development of a machine learning-based model to predict urethral recurrence following radical cystectomy: a multicentre retrospective study and updated meta-analysis

Fig. 2

Feature selection using the LASSO regression model. (A) Tenfold cross-validation was performed to identify the optimal penalization coefficient (lambda) for the LASSO model. (B) The plots illustrate the LASSO regression coefficients across a range of penalty parameter values. In our study, lambda.min was selected for its stricter penalty, which effectively mitigates overfitting. LASSO, least absolute shrinkage and selection operator.

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