scikit-learn cross validation, negative values with mean squared error
cross-validation, python, regression, scikit-learn
Solution
Trying to close this out, so am providing the answer that David and larsmans have eloquently described in the comments section:
Yes, this is supposed to happen. The actual MSE is simply the positive version of the number you're getting.
The unified scoring API always maximizes the score, so scores which need to be minimized are negated in order for the unified scoring API to work correctly. The score that is returned is therefore negated when it is a score that should be minimized and left positive if it is a score that should be maximized.
This is also described in sklearn GridSearchCV with Pipeline.
Problem
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 returns negative values, which is unexpected. Do you have any idea? ``` 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='mean_squared_error') ``` all values in `scores` are then negative.