On 05/03/2017 08:05 AM, 熊瑶 wrote:
Dear professor,
scikit-learn is really good for me to do some work using machine
learning method. Here, I have two questions:
1）To do 5 fold cross-validation, when I use StratifiedKFold，I could
get stratified folds that each fold contains approximately the same
percentage of samples
of each target class as the complete set. And, when I use GroupKFold,
it ensures that the same group is not represented in both testing and
training sets.
I want to know whether there is a method to combine these two methods
together?
Not implemented (yet). I think because it was a bit unclear what's the
best thing to do.
2) When I use GridSearchCV to do parameter search, I use
scoring="accuracy" as scoring function to choose the best parameters.
And I find that I can only get
the accuracy score from the 5 fold cross-validation. What can I do if
I want to get other scores such as sensitivity, specificity, MCC *at
the same time*? It
means that I want to use accuracy to choose the best parameters and I
want to get the scores of many scoring parameters at the same time
when I do 5 fold
https://github.com/scikit-learn/scikit-learn/pull/7388
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