CalibratedBinaryClassificationMetrics.LogLoss Property

Definition

Namespace:

Microsoft.ML.Data

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v3.0.1

Gets the log-loss of the classifier. Log-loss measures the performance of a classifier with respect to how much the predicted probabilities diverge from the true class label. Lower log-loss indicates a better model. A perfect model, which predicts a probability of 1 for the true class, will have a log-loss of 0.

public double LogLoss { get; }

member this.LogLoss : double

Public ReadOnly Property LogLoss As Double

Property Value

Double

Remarks

The log-loss metric, is computed as follows: $LogLoss = - \frac{1}{m} \sum{i = 1}^m ln(p_i)$ where m is the number of instances in the test set and $p_i$ is the probability returned by the classifier if the instance belongs to class 1, and 1 minus the probability returned by the classifier if the instance belongs to class 0.