DALEX (version 1.3.0)

model_parts: Dataset Level Variable Importance as Change in Loss Function after Variable Permutations

Description

From DALEX version 1.0 this function calls the feature_importance Find information how to use this function here: https://pbiecek.github.io/ema/featureImportance.html.

Usage

model_parts(
  explainer,
  loss_function = loss_default(explainer$model_info$type),
  ...,
  type = "variable_importance",
  N = n_sample,
  n_sample = 1000
)

Arguments

explainer

a model to be explained, preprocessed by the explain function

loss_function

a function that will be used to assess variable importance. By default it is 1-AUC for classification, cross entropy for multilabel classification and RMSE for regression.

...

other parameters

type

character, type of transformation that should be applied for dropout loss. variable_importance and raw results raw drop lossess, ratio returns drop_loss/drop_loss_full_model while difference returns drop_loss - drop_loss_full_model

N

number of observations that should be sampled for calculation of variable importance. If negative then variable importance will be calculated on whole dataset (no sampling).

n_sample

alias for N held for backwards compatibility. number of observations that should be sampled for calculation of variable importance.

Value

An object of the class feature_importance. It's a data frame with calculated average response.

References

Explanatory Model Analysis. Explore, Explain and Examine Predictive Models. https://pbiecek.github.io/ema/

Examples

# NOT RUN {
 
# }
# NOT RUN {
library("ranger")
titanic_ranger_model <- ranger(survived~., data = titanic_imputed, num.trees = 50,
                               probability = TRUE)
explainer_ranger  <- explain(titanic_ranger_model, data = titanic_imputed[,-8],
                             y = titanic_imputed$survived)
vi_ranger <- model_parts(explainer_ranger, type = "raw")
head(vi_ranger, 8)
plot(vi_ranger)

titanic_glm_model <- glm(survived~., data = titanic_imputed, family = "binomial")
explainer_glm <- explain(titanic_glm_model, data = titanic_imputed[,-8],
                         y = titanic_imputed$survived)
logit <- function(x) exp(x)/(1+exp(x))
vi_glm <- model_parts(explainer_glm, type = "raw",
                        loss_function = function(observed, predicted)
                                     sum((observed - logit(predicted))^2))
head(vi_glm, 8)
plot(vi_glm)
 
# }
# NOT RUN {
# }