# 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