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_auc(observed, predicted) loss_default(x)

observed | observed scores or labels, these are supplied as explainer specific |
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predicted | predicted scores, either vector of matrix, these are returned from the model specific |

p_min | for cross entropy, minimal value for probability to make sure that |

na.rm | logical, should missing values be removed? |

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$survived, yhat(titanic_ranger_model, titanic_imputed))#> [1] 0.1013152HR_ranger_model_multi <- ranger(status~., data = HR, num.trees = 50, probability = TRUE) loss_cross_entropy(as.numeric(HR$status), yhat(HR_ranger_model_multi, HR))#> [1] 2994.432# }