Predicting chemotherapy-induced myelosuppression in colorectal cancer: An interpretable, machine learning-based nomogram

Figure 6 Curves for 10 machine learnings. A and B: Receiver operating characteristic curves of training set (A) and validation set (B); C and D: Precision-recall curves of training set (C) and validation set (D). LR: Logistic regression; DT: Decision trees; RF: Random forest; XGBoost: Extreme gradient boosting; SVM: Support vector machines; GBM: Gradient boosting machines; KNN: K-Nearest neighbors; ANN: Artificial neural network; ET: Extreme trees; AUC: Area under the curve; AP: Average precision.