Hmm, perhaps I am going about this all wrong.
What I'd like to do is assess whether "site" impacts the predictability of the
data. Wouldn't a LOLO approach (with labels corresponding to sites) be a
viable way to assess this? Lower values for R^2 would indicate that a site is
not producing data that are reliable with other sites?
On May 5, 2013, at 6:30 PM, Joel Nothman wrote:
> Apart from the scores, I'm not sure what you are trying to calculate with
> cross_val_score here: you're passing it a single LOLO fold's test data, i.e.
> corresponding to one label from labels. It is reporting the results of
> three-fold cross validation over that sample. Is that what you intended? -
> Joel
>
>
> On Sat, May 4, 2013 at 12:41 AM, John Richey <ric...@vt.edu> wrote:
> Hi all -
>
> I am relatively new to the world of machine learning, and I am having a
> little difficulty in interpreting the output of a support vector regression
> problem. For simplicity, lets say I have 2 variables and 100 subjects.
> Both variables in my model are continuous.
>
> To make matters a little more complicated, I have four "sites" at which data
> were collected, and I want to "leave one label out", where labels correspond
> to sites for the purposes of assessing whether site has an influence on the
> predictive model.
>
> Here is the code so far.
>
>
>
> lolo = LeaveOneLabelOut(labels)
>
> for train_index, test_index in lolo:
>
> X_train, X_test = X[train_index], X[test_index]
> y_train, y_test = y[train_index], y[test_index]
>
> clf = svm.SVR()
> clf = clf.fit(X_train, y_train)
> s=clf.score(X_test, y_test)
> print s
>
> scores = cross_validation.cross_val_score(clf,X_test, y_test)
>
> print "Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2)
>
>
> It produces the following output
> 0.0343889480748
> Accuracy: -0.05 (+/- 0.05)
> -0.0786771792262
> Accuracy: -0.25 (+/- 0.07)
> -0.0871562121791
> Accuracy: -0.12 (+/- 0.05)
> -0.0496675695436
> Accuracy: -0.16 (+/- 0.03)
>
>
>
>
>
>
> Could someone help me in how to interpret the substantive meaning of the
> 'score' in an SVR problem? Thanks in advance.
>
>
>
>
>
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