# loss_functions: Calculate Loss Functions In DALEX: moDel Agnostic Language for Exploration and eXplanation

 loss_cross_entropy R Documentation

## 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

```
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))

```

DALEX documentation built on Jan. 16, 2023, 1:06 a.m.