Classification measure defined as $$
-\frac{1}{n} \sum_{i=1}^n \log \left( p_i \right )
$$
where \(p_i\) is the probability for the true class of observation \(i\).

logloss(truth, prob, eps = 1e-15, ...)

## Arguments

truth |
:: `factor()`
True (observed) labels.
Must have the same levels and length as `response` . |

prob |
:: `matrix()`
Matrix of predicted probabilities, each column is a vector of probabilities for a
specific class label.
Columns must be named with levels of `truth` . |

eps |
:: `numeric(1)`
Probabilities are clipped to `max(eps, min(1 - eps, p))` .
Otherwise the measure would be undefined for probabilities `p = 0` and `p = 1` . |

... |
:: `any`
Additional arguments. Currently ignored. |

## Value

Performance value as `numeric(1)`

.

## See also

## Examples

#> [1] 1.33052