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.
------------------------------------------------------------------------------
Get 100% visibility into Java/.NET code with AppDynamics Lite
It's a free troubleshooting tool designed for production
Get down to code-level detail for bottlenecks, with <2% overhead.
Download for free and get started troubleshooting in minutes.
http://p.sf.net/sfu/appdyn_d2d_ap2
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
