br-test.arffdata. Train your algorithm on the entire training set using your chosen parameter values, and evaluate on the BR test set.
For the RBF kernel, the parameter is "gamma", which controls the width of the RBF kernel. Values in the range from 1 to 10 usually work well, but sometimes values as small at 0.1 or as large as 50 give good results.
confidence level tree size validation error ppp sss eeewith one row for each confidence level that you tried. The tree size is the total number of nodes, and it is reported by the algorithm. Finally, report your chosen confidence level, the resulting tree size (when trained on the entire training set), and the test set error.
ridge parameter sum of abs(coef) validation error ppp sss eeewith one row for each ridge value that you tried. The second column is the sum of the absolute values of the coefficients (not including the intercept term). You will need to compute this from the output produced by the algorithm. Finally, report your chosen ridge parameter, the resulting sum of abs(coef), and the test set error when training on the entire training set.
C kernel kernel-params validation error ccc kkk ppp eeewhere
kkkis "polynomial" or "rbf" and
pppis the parameter value of the kernel (exponent for polynomial and gamma for rbf). Include one line for each combination of C, kernel, and kernel parameters that you tried. Finally, of course, report your chosen parameters and the test set error when training on the entire training set.
java -Xmx200m -jar weka.jarto request 200 megabytes of memory for the java vm.