search for: svmmodel2

...col=colors, pch=20) lines(x, f(x), col = colors[1]) # overlay noiseless data # SVM, untuned svmmodel1 <- svm(x, y) print(summary(svmmodel1)) y1 <- predict(svmmodel1, x) lines(x, y1, col = colors[2]) # SVM with tuning tuning <- tune.svm(x, y, gamma = 2^(-4:4), cost = 2^(-2:2)) svmmodel2 <- tuning$best.model print(summary(svmmodel2)) y2 <- predict(svmmodel2, x) lines(x, y2, col = colors[3])