# CalibratedBinaryClassificationMetrics.LogLossReduction Property

## Definition

Gets the log-loss reduction (also known as relative log-loss, or reduction in information gain - RIG) of the classifier. It gives a measure of how much a model improves on a model that gives random predictions. Log-loss reduction closer to 1 indicates a better model.

`public double LogLossReduction { get; }`

`member this.LogLossReduction : double`

`Public ReadOnly Property LogLossReduction As Double`

#### Property Value

## Remarks

The log-loss reduction is scaled relative to a classifier that predicts the prior for every example: $LogLossReduction = \frac{LogLoss(prior) - LogLoss(classifier)}{LogLoss(prior)} This metric can be interpreted as the advantage of the classifier over a random prediction. For example, if the RIG equals 0.2, it can be interpreted as "the probability of a correct prediction is 20% better than random guessing".