Table 2.

Table 2 is all model we used to predict micropapillary/solid component of lung adenocarcinoma, LR model preforms the best performance. So in the building of clinical signature, LR is selected as base model.

Model_name	Accuracy	AUC	95% CI	Sensitivity	Specificity	PPV	NPV	Task
LR	0.839	0.858	0.8007–0.9158	0.870	0.735	0.919	0.621	Label-train
LR	0.636	0.822	0.7036–0.9398	0.535	1.000	1.000	0.375	Label-test
SVM	0.927	0.954	0.9122–0.9948	0.917	0.959	0.987	0.770	Label-train
SVM	0.800	0.806	0.6509–0.9615	0.791	0.833	0.944	0.526	Label-test
KNN	0.789	0.854	0.8052–0.9035	0.817	0.694	0.902	0.523	Label-train
KNN	0.764	0.811	0.6964–0.9257	0.791	0.667	0.895	0.471	Label-test
RandomForest	0.995	1.000	0.9996–1.0000	0.994	1.000	1.000	0.980	Label-train
RandomForest	0.836	0.785	0.6274–0.9424	0.884	0.667	0.905	0.615	Label-test
ExtraTrees	1.000	1.000	1.0000–1.0000	1.000	1.000	1.000	1.000	Label-train
ExtraTrees	0.800	0.673	0.4840–0.8629	0.907	0.455	0.848	0.556	Label-test
XGBoost	1.000	1.000	1.0000–1.0000	1.000	1.000	1.000	1.000	Label-train
XGBoost	0.764	0.771	0.6251–0.9176	0.791	0.667	0.895	0.471	Label-test
LightGBM	0.876	0.952	0.9267–0.9777	0.852	0.959	0.986	0.653	Label-train
LightGBM	0.618	0.767	0.6213–0.9136	0.535	0.917	0.958	0.355	Label-test
MLP	0.908	0.932	0.8940–0.9703	0.941	0.796	0.941	0.796	Label-train
MLP	0.782	0.818	0.6994–0.9363	0.791	0.750	0.919	0.500	Label-test

KNN, k-nearest neighbor; LightGBM, light gradient boosting machine; LR, logistic regression; MLP, multilayer perceptron; NPV negative predictive value; SVM, support vector machines; PPV, positive predictive value.