r ROC性能比较

Posted 2021-05-24

#Compare ROC Performance of Models
plot(m1_perf, col='blue', lty=1, main='ROCs: Model Performance Comparision') # logistic regression
plot(m2_perf, col='gold',lty=2, add=TRUE); # simple tree
plot(m2_1_perf, col='dark orange',lty=3, add=TRUE); #tree with 90/10 prior
plot(m3_perf, col='green',add=TRUE,lty=4); # random forest
plot(m4_perf, col='dark gray',add=TRUE,lty=5); # Conditional Inference Tree
plot(m3_2_perf, col='dark green',add=TRUE,lty=6); # Improved logistic regression using random forest
plot(m7_2_perf, col='black',add=TRUE,lty=7); # Support Vector Machine (SVM)
plot(m8_perf, col='red',add=TRUE,lty=8); # Neural Network
plot(m9_2_perf, col='brown',add=TRUE,lty=9); # Neural Network
    legend(0.6,0.5,
           c('m1:logistic reg','m2:Recursive Partitioning','m2_1:Recursive Partitioning - Bayesian', 
              'm3:random forest', "m4:condtn inference tree", "m3_2:Improved Logistic", "m7_2:SVM", 
              "m8:Neural Net", "m9:Lasso"),
           col=c('blue','gold', 'orange','green', 'dark gray', 'dark green', "black", "red","brown"),
           lwd=3);
lines(c(0,1),c(0,1),col = "gray", lty = 4 ) # random line