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.
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.
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')
To see what are available scoring keys use:
import sklearn print(sklearn.metrics.SCORERS.keys())
You can either use
'r2' or 'neg_mean_squared_error'. There are lots of options based on your requirement.