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)
```

- 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".

numeric - value of the loss function

```
# \donttest{
library("ranger")
titanic_ranger_model <- ranger(survived~., data = titanic_imputed, num.trees = 50,
probability = TRUE)
loss_one_minus_auc(titanic_imputed$survived, yhat(titanic_ranger_model, titanic_imputed))
#> [1] 0.1050095
HR_ranger_model_multi <- ranger(status~., data = HR, num.trees = 50, probability = TRUE)
loss_cross_entropy(as.numeric(HR$status), yhat(HR_ranger_model_multi, HR))
#> [1] 2960.559
# }
```