DALEX (version 2.3.0)

loss_cross_entropy: Calculate Loss Functions

Description

Calculate Loss Functions

Usage

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_auc(observed, predicted)
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?

x

either an explainer or type of the model. One of "regression", "classification", "multiclass".

Value

numeric - value of the loss function

Examples

# NOT RUN {
 
# }
# NOT RUN {
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))

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))

 
# }