fig10

Knowledge-enabled data-driven smart design of ultra-strong ductile near-α titanium alloys under extreme conditions

Figure 10. A performance comparison chart of 12 machine learning model test sets constructed based on the optimal plastic strain feature subset. ML: Machine learning; R2: the coefficient of determination; RMSE: root mean square error; RF: random forest; Bagging: bootstrap aggregating; ExtraTree: extremely randomized tree; XGBoost: eXtreme gradient boosting; DT: decision tree; KNN: K-nearest neighbor; GBRT: gradient boosting regression tree; LGBM: light gradient boosting machine; AdaBoost: adaptive boosting; ANN: artificial neural network; SVR: support vector regression.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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