precrec (version 0.24.0)

prob_metrics_ci: Calculate CIs of the Brier score and the log loss

Description

The prob_metrics_ci function calculates the confidence intervals of the Brier score and the log loss when multiple test datasets are specified.

Usage

prob_metrics_ci(
  mdat,
  scores = NULL,
  labels = NULL,
  eps = 1e-15,
  alpha = 0.05,
  dtype = "normal",
  metrics = NULL,
  ...
)

Value

The prob_metrics_ci function returns a data frame with the columns modnames, metrics, mean, error, lower_bound, upper_bound, and n.

Arguments

mdat

An S3 object created by the mmdata() function. It contains formatted scores and labels. The prob_metrics function ignores scores and labels when mdat is specified. These arguments are internally passed to the mmdata() function when mdat is unspecified. In that case, both scores and labels must be at least specified.

scores

A numeric dataset of predicted probabilities. It can be a vector, a matrix, an array, a data frame, or a list.

labels

A numeric, character, logical, or factor dataset of observed labels. It can be a vector, a matrix, an array, a data frame, or a list.

eps

A numeric value used to clamp the scores away from 0 and 1 before the log loss is calculated. A single confident and wrong prediction would otherwise make the log loss infinite.

alpha

A numeric value of the significant level (default: 0.05)

dtype

A string to specify the distribution used for CI calculation.

dtype	distribution
normal (default)	Normal distribution
z	Normal distribution
t	t-distribution

metrics

A character vector of additional metrics to calculate. The three metrics above are always returned; "d2_brier" and "d2_logloss" are returned as well when they are named here, and "all" asks for every metric the function knows.

A D2 score rescales a loss against the loss of the null model, the one that predicts the observed prevalence for every case and ignores the scores:

D2 = 1 - loss(model) / loss(null)

It is 1 for a perfect model and 0 for one that does no better than the prevalence, and it is negative for a model that does worse - which is a real result rather than an error, so it is not clipped. A dataset holding a single class has a null loss of 0 and so a D2 score of NA.

...

These additional arguments are passed to mmdata() for data preparation.

See Also

prob_metrics() for the per-dataset metrics themselves. auc_ci() for the equivalent calculation on AUC scores.

Examples

##################################################
### Single model & multiple test datasets
###

## Create sample datasets with 100 positives and 100 negatives
samps <- create_sim_samples(4, 100, 100, "good_er")
mdat <- mmdata(samps[["scores"]], samps[["labels"]],
  modnames = samps[["modnames"]],
  dsids = samps[["dsids"]]
)

## Calculate the CIs
prob_metrics_ci(mdat)