As continued from the Supervised Classification Post, we have been running grid search on the email classification algorithm varying Gamma and C.  The result are as below. Each column represents a Gamma value and each row represents a C value. The value of the internal grids themselves are error rate percentage for the given value of Gamma and C.

The eye-opening observation of this experiment is how much Gamma influences the error rate compared to C.  While any value of C less than 1 is bad, probably due to over fitting, the performance of the Classification Algorithm does not vary much where C >= 1.  Whereas, distinct gamma values have a more concrete effect on the error value of the Classifcation.

Test data:

I took the parameters pertaining to the lowest error value (3.75) resulting in gamma value at 0.03125 and C value at 4.  I use these values to evaluate the algorithm on the test data. The resultant Error rate is 4.6%

Gamma -> C	0.0009765625	0.03125	0.25	1	4
0.03125	18.15	9.75	44.15	46.2	46.4
0.25	7.25	5.4	18.05	14.2	38.15
1	5.65	4.15	11	14.85	17
4	4.6	3.75	10.95	14.55	17
32	3.95	3.95	10.95	14.55	17
1024	3.9	4.15	10.95	14.55	17