This function calculates log loss/cross-entropy loss for binary models. NOTE: when result is 0.69315, the classification is neutral; it assigns equal probability to both classes.
Arguments
- tag
Vector. Real known label
- score
Vector. Predicted value or model's result
- eps
Numeric. Epsilon value
See also
Other Model metrics:
ROC()
,
conf_mat()
,
errors()
,
gain_lift()
,
model_metrics()