Fig. 4: Comparison of the XGBoost, topogivity, and t-SNE approaches. | npj Computational Materials

Fig. 4: Comparison of the XGBoost, topogivity, and t-SNE approaches.

From: Machine learning on multiple topological materials datasets

Fig. 4: Comparison of the XGBoost, topogivity, and t-SNE approaches.The alternative text for this image may have been generated using AI.

Receiver operating characteristic (ROC) and precision-recall curves for distinguishing nontrivial topological materials (NTMs) from trivial insulators (TrIs) on the dataset M ∪ T.

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