logloss: Calculate logloss for evaluating predictions
In dclaz/mELO: Multidimensional Elo Ratings

Description Usage Arguments Value Examples

Calculate logloss or cross-entropy for a set of predictions.

1	logloss(prediction, outcome, tol = .Machine$double.neg.eps)

`prediction`	A vector of estimated probabilities.
`outcome`	A vector of observed outcomes.
`tol`	Numerical tolerance. Can also be used to threshold errors for really bad predictions, or when you don't want a model to be penalized too strongly in the presence of high dispersion. Default is .Machine$double.neg.eps.

a numeric vector

preds <- c(0.9, 0.1, 0.8, 0.5)
outcomes <- c(1, 0, 1, 0)
logloss(preds, outcomes)

# Thresholding large errors for bad predictions
preds <- c(0.000001)
outcomes <- c(1)
logloss(preds, outcomes)
logloss(preds, outcomes, 0.01)