# Metrics

**Metric importance**: Measures the *strength* of the dependency between the metric and model
performance. A high value means that the model performance would be strongly affected by
a change in the metric. For example, high importance in 'Brightness' implies that a change
in that quantity would strongly affect model performance. Values range from 0 (no dependency)
to 1 (perfect dependency, one can completely predict model performance simply by looking
at this metric).

**Metric correlation**: Measures the *linearity
and direction* of the dependency between a metric and model performance.
Crucially, this metric tells us whether a positive change in a metric
will lead to a positive change (positive correlation) or a negative change (negative correlation)
in model performance. Values range from -1 to 1.