Logarithmic loss

Logarithmic loss gives the mean of logarithms target class probabilities (confidence). It is also known as Expected log-likelihood and is an effective measure of model performance.

Logarithmic loss at a glance

  • Description: Mean of logarithms target class probabilities (confidence). It is also known as Expected log-likelihood.
  • Default thresholds: Lower limit = 80%
  • Default recommendation:
    • Upward trend: An upward trend indicates that the metric is deteriorating. Feedback data is becoming significantly different than the training data.
    • Downward trend: A downward trend indicates that the metric is improving. This means that model retraining is effective.
    • Erratic or irregular variation: An erratic or irregular variation indicates that t The feedback data is not consistent between evaluations. Increase the minimum sample size for the Quality monitor.
  • Problem type: Binary classification and multiclass classification
  • Chart values: Last value in the time frame
  • Metrics details available: None

Do the math

For a binary model, Logarithmic loss is calculated by using the following formula:

-(y log(p) + (1-y)log(1-p))

where p = true label and y = predicted probability

For a multi-class model, Logarithmic loss is calculated by using the following formula:

  M
-SUM Yo,c log(Po,c)
 c=1 

where M > 2, p = true label, and y = predicted probability