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...tection) parameter : nu = 0.05 Gaussian Radial Basis kernel function. Hyperparameter : sigma = 0.1 Number of Support Vectors : 660 Objective Function Value : 10.5781 Training error : 0.212907 Cross validation error : 0 --- What surprises me is that the Training error (which I suppose is the resubstitution error) is higher than the cross-validation error. Also, even changing the value of sigma, the cross-validation error does not change. I get similar results with other datasets, too. For example classifier <- ksvm(Species~.,data=iris[which(iris[,'Species']=='setosa'),], type=&...

I?ve been using Frank Harrell?s rms package to do bootstrap model validation. Is it the case that the optimum penalization may still give a model which is substantially overfitted? I calculated corrected R^2, optimism in R^2, and corrected slope for various penalties for a simple example: x1 <- rnorm(45) x2 <- rnorm(45) x3 <- rnorm(45) y <- x1 + 2*x2 + rnorm(45,0,3) ols0 <- ols(y