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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.