scikit-learn cross validation, negative values with mean squared error

Question

When I use the following code with Data matrix X of size (952,144) and output vector y of size (952), mean_squared_error metric return

Answer 1

You can fix it by changing scoring method to "neg_mean_squared_error" as you can see below:

from sklearn.svm import SVR
from sklearn import cross_validation as CV

reg = SVR(C=1., epsilon=0.1, kernel='rbf')
scores = CV.cross_val_score(reg, X, y, cv=10, scoring='neg_mean_squared_error')