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… shape with unidimensional outputs. Replace denominator of scoring function with np.var(y). This means samples with equal error will have equal score. It also allows the average value of the samples to equal the r2_score for the entire set in the case of unidimensional data.
… shape with unidimensional outputs. Replace denominator of scoring function with np.var(y). This means samples with equal error will have equal score. It also allows the average value of the samples to equal the r2_score for the entire set in the case of unidimensional data.
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I've merged this into the branch v0.2dev. Unfortunately, the latest version of scikit-learn's check_estimator test requires that the score_samples method operate on If anyone is looking for this functionality now, I would suggest just working directly off of this PR. Note: this PR was in response to the discussion in issue #182. |
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This version of score_samples scores each sample on a coordinate-by-coordinate basis.
It follows the internals of scikit-learn's r2_score implementation in that coordinates that have no variance are arbitrarily set to a score of 1. This prevents division by zero and -inf scores.