loss_functions: Calculate Loss Functions

In ModelOriented/DALEX: moDel Agnostic Language for Exploration and eXplanation

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_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)

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?
cutoff	classification threshold for the accuracy loss functions
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))

 

ModelOriented/DALEX documentation built on Oct. 13, 2024, 3:32 p.m.