Hi John.
For regression problems, the score is the R^2, the coefficient of
determination:
https://en.wikipedia.org/wiki/Coefficient_of_determination
as is explained in the documentation
http://scikit-learn.org/dev/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR.score
Negative scores basically mean you learned nothing.
You can also use MSE for regression problems (using
sklearn.metrics.mean_squared_error or scoring='mse' in the newest version),
which I find a bit easier to interpret.
Hth,
Andy
On 05/03/2013 04:41 PM, John Richey 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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