## ## Parameter tuning of 'svm': ## ## - sampling method: 10-fold validation ## ## - best parameters: ## cost ## 100 ## ## - best performance: 0.008807 ## ## - Detailed performance results: ## cost error dispersion ## 1 1e-03 0.692598 0.020054 ## 2 1e-02 0.100834 0.017639 ## 3 1e-01 0.033999 0.008356 ## 4 1e+00 0.015599 0.005558 ## 5 5e+00 0.011998 0.004413 ## 6 1e+01 0.011801 0.004842 ## 7 1e+02 0.008807 0.004352 # examine the best model bestmodR = tune.outR$best.model summary(bestmodR) ## ## Call: ## best.tune(method = svm, = colors5 ~ ., data = dat, ranges = list(cost = c(0.001, ## 0.01, 0.1, 1, 5, 10, 100)), kernel = "radial", gamma = 1) ## ## ## Parameters: ## SVM-Type: C-classification ## SVM-Kernel: radial ## cost: 100 ## gamma: 1 ## ## Number of Support Vectors: 286 ## ## ( 43 70 37 68 68 ) ## ## ## Number of Classes: 5 ## ## Levels: ## blue cyan green orange purple
## ## Parameter tuning of 'svm': ## ## - sampling method: 10-fold validation ## ## - best parameters: ## cost ## 100 ## ## - best performance: 0.008807 ## ## - Detailed performance results: ## cost error dispersion ## 1 1e-03 0.692598 0.020054 ## 2 1e-02 0.100834 0.017639 ## 3 1e-01 0.033999 0.008356 ## 4 1e+00 0.015599 0.005558 ## 5 5e+00 0.011998 0.004413 ## 6 1e+01 0.011801 0.004842 ## 7 1e+02 0.008807 0.004352 # examine the best model bestmodR = tune.outR$best.model summary(bestmodR) ## ## Call: ## best.tune(method = svm, = colors5 ~ ., data = dat, ranges = list(cost = c(0.001, ## 0.01, 0.1, 1, 5, 10, 100)), kernel = "radial", gamma = 1) ## ## ## Parameters: ## SVM-Type: C-classification ## SVM-Kernel: radial ## cost: 100 ## gamma: 1 ## ## Number of Support Vectors: 286 ## ## ( 43 70 37 68 68 ) ## ## ## Number of Classes: 5 ## ## Levels: ## blue cyan green orange purple
IraAppliedStats.docx