scikit-learn score metric on the coefficient of determination $R^2$ I am using scikit-learn in Python and they define a quantity called score. It's defined in the middle of the documentation page.
Reproduced here:

Returns the coefficient of determination R^2 of the prediction.
  The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.

A few questions regarding this:


*

*What's the intuition behind this metric?

*What is considered a good score? What is considered bad?
 A: Consider using the precision and recall scores of scikit-learn: http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html. It may give you a more tangible number to consider.
Precision and recall are defined as:
The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.
The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.
I often too find the score function of the classifiers to be somewhat abstract/ not applicable to my usecase, but the precision and recall gives you a percentage of how many of the predicted items was actually predicted correctly, and how many did the classifier miss.
A: The Coefficient of Determination (R^2) generalizes the correlation coefficient (r) to multiple predictors, and is often summarized as the proportion of variance explained by the model.  It will be quite comfortable for anyone used to analyzing linear regression models, and will be discussed in any text or course you might have takem.
1.0 is a perfect score, good is relative.
Note that the answer which discusses precision and recall does not answer the question posed, which was about Support Vector Regression, not Support Vector Classification.  True Positive and False Positive assume binary (True/False) responses.
