mc_lloss: Calculate the multiclass logarithmic loss for predicted...

In gweissman/gmish: Miscellaneous Functions for Predictive Modeling

Calculate the multiclass logarithmic loss for predicted probabilities against a binary outcome.

Description

Calculate the multiclass logarithmic loss for predicted probabilities against a binary outcome.

Usage

mc_lloss(preds, obs, eps = 1e-15)

Arguments

preds	A data.frame or matrix of predicted probabilities with one column per class.
obs	A data.frame or matrix containing the observed binary outcomes (0 or 1) with one column per class. The order of the columns should match the order for preds.
eps	Epsilon representing the tolerance of the numeric result, used in order to avoid zero errors.

Value

The Log Loss given by

logloss = y_i(\log \hat y_i) + (1-y_i)\log(1-\hat y_i)

summed over each class. #' @examples # Generate some predictions predictions <- data.frame(p1 = runif(1000), p2 = runif(1000), p3 = runif(1000)) # Generate some binary outcomes observations <- data.frame(o1 = sample(0:1, size = 1000, replace = TRUE), o2 = sample(0:1, size = 1000, replace = TRUE), o3 = sample(0:1, size = 1000, replace = TRUE)) # Calculate the multiclass Logarithmic Loss mc_lloss(predictions, observations)

gweissman/gmish documentation built on Feb. 21, 2025, 1:20 a.m.