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Insights into Imaging
Fig. 2 | Insights into Imaging

Fig. 2

From: A clinical–radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study

Fig. 2

Flowchart of the most important features’ selection (The numbers in parentheses are characteristic numbers; ICC, Intercorrelation Coefficient; LASSO, Least Absolute Shrinkage and Selection Operator; RFECV, Recursive Feature Elimination Cross Validation; RFE, Recursive Feature Elimination; LR, Logistic Regression; Linear SVC, Linear Support Vector Classification; SGD, Stochastic Gradient Descent; SVM, Support Vector Machine; RF, Random Forest; XGB, eXtreme Gradient Boosting)

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