So every time I do a GridSearchCV
with KFold, stratified or not, I get the same accuracy score and STDev for values of C=1,C=10, and C=100. I then did a special test without the grid search and used stratified k-fold but this time testing individual accuracy per each fold. It does not matter the amount of folds, the result is the same - same exact accuracy for different C values. Is there any reason why this could be? It doesn't happen with a shuffle split cross-validation, for example.
If it makes any difference, my classes are in order (first 40 are the same, second 40 are the same, 3rd 40 are the same in a set of 120), but even the shuffled k-fold yields the same results.
Sample:
[mean: 0.60000, std: 0.11365, params: {'C': 0.001},
mean: 0.69167, std: 0.12472, params: {'C': 0.01},
mean: 0.74167, std: 0.08498, params: {'C': 0.1},
mean: 0.76667, std: 0.01179, params: {'C': 1},
mean: 0.75833, std: 0.01179, params: {'C': 10},
mean: 0.75833, std: 0.01179, params: {'C': 100},
mean: 0.75833, std: 0.01179, params: {'C': 1000}]