- mne.simulation.metrics.precision_score(stc_true, stc_est, threshold='90%', per_sample=True)[source]#
Compute the precision.
The precision is the ratio
tp / (tp + fp)where
tpis the number of true positives and
fpthe number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.
The best value is 1 and the worst value is 0.
Threshold is used first for data binarization.
- stc_trueinstance of (Vol|Mixed)SourceEstimate
The source estimates containing correct values.
- stc_estinstance of (Vol|Mixed)SourceEstimate
The source estimates containing estimated values e.g. obtained with a source imaging method.
The threshold to apply to source estimates before computing the precision. If a string the threshold is a percentage and it should end with the percent character.
If True the metric is computed for each sample separately. If False, the metric is spatio-temporal.
New in version 1.2.
Compare simulated and estimated source activity