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

## Description

Calculate Loss Functions

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```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

 ``` 1 2 3 4 5 6 7 8 9 10``` ``` 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)) ```

ModelOriented/DALEX documentation built on June 14, 2021, 2:21 a.m.