模型	方法	Accuracy	Precision	Recall	F₁	Time
n = 200
Model 1	MKL	0.833	0.957	0.710	0.815	0.024
	Logistic	0.850	0.923	0.774	0.842	0.002
	SVM	0.767	0.742	0.793	0.767	0.008
	RF	0.717	0.733	0.710	0.721	0.036
	Net	0.850	0.871	0.844	0.857	0.324
Model 2	MKL	0.883	0.778	0.955	0.857	0.007
	Logistic	0.900	0.786	1.000	0.880	0.003
	SVM	0.917	1.000	0.815	0.900	0.007
	RF	0.900	0.681	0.955	0.875	0.033
	Net	0.833	0.955	0.833	0.750	0.330
Model 3	MKL	0.817	0.782	0.750	0.766	0.006
	Logistic	0.767	0.632	1.000	0.774	0.003
	SVM	0.683	0.500	0.632	0.558	0.008
	RF	0.733	0.700	0.5 83	0.636	0.038
	Net	0.783	0.833	0.690	0.755	0.235
n = 1000
Model 1	MKL	0.813	0.843	0.812	0.827	0.026
	Logistic	0.843	0.801	0.952	0.870	0.004
	SVM	0.770	0.776	0.800	0.788	0.046
	RF	0.803	0.812	0.837	0.824	0.195
	Net	0.803	0.806	0.831	0.818	1.137
Model 2	MKL	0.913	0.868	0.956	0.910	0.032
	Logistic	0.923	0.865	0.985	0.922	0.004
	SVM	0.890	0.869	0.888	0.878	0.025
	RF	0.870	0.895	0.810	0.851	0.161
	Net	0.930	0.956	0.897	0.926	1.571
Model 3	MKL	0.823	0.729	0.851	0.785	0.026
	Logistic	0.803	0.687	0.886	0.773	0.003
	SVM	0.723	0.719	0.617	0.664	0.034
	RF	0.777	0.704	0.711	0.707	0.169
	Net	0.787	0.974	0.645	0.776	1.432