Calculate Loss Functions

loss_cross_entropy(observed, predicted, p_min = 1e-04, na.rm = TRUE)

loss_sum_of_squares(observed, predicted, na.rm = TRUE)

loss_root_mean_square(observed, predicted, na.rm = TRUE)

loss_accuracy(observed, predicted, na.rm = TRUE)

loss_one_minus_accuracy(observed, predicted, cutoff = 0.5, na.rm = TRUE)

get_loss_one_minus_accuracy(cutoff = 0.5, na.rm = TRUE)

loss_one_minus_auc(observed, predicted)

get_loss_default(x)

loss_default(x)

Arguments

observed: observed scores or labels, these are supplied as explainer specific y
predicted: predicted scores, either vector of matrix, these are returned from the model specific predict_function()
p_min: for cross entropy, minimal value for probability to make sure that log will not explode
na.rm: logical, should missing values be removed?
cutoff: classification threshold for the accuracy loss functions
x: either an explainer or type of the model. One of "regression", "classification", "multiclass".

Value

numeric - value of the loss function

Examples

 # \donttest{
library("ranger")
titanic_ranger_model <- ranger(survived~., data = titanic_imputed, num.trees = 50,
                               probability = TRUE)
loss_one_minus_auc(titanic_imputed$survived, yhat(titanic_ranger_model, titanic_imputed))
#> [1] 0.1036886

HR_ranger_model_multi <- ranger(status~., data = HR, num.trees = 50, probability = TRUE)
loss_cross_entropy(as.numeric(HR$status), yhat(HR_ranger_model_multi, HR))
#> [1] 2979.306

 # }