DALEX (version 2.4.3)

predict_diagnostics: Instance Level Residual Diagnostics

Description

This function performs local diagnostic of residuals. For a single instance its neighbors are identified in the validation data. Residuals are calculated for neighbors and plotted against residuals for all data. Find information how to use this function here: https://ema.drwhy.ai/localDiagnostics.html.

Usage

predict_diagnostics(
  explainer,
  new_observation,
  variables = NULL,
  ...,
  nbins = 20,
  neighbors = 50,
  distance = gower::gower_dist
)
individual_diagnostics(
  explainer,
  new_observation,
  variables = NULL,
  ...,
  nbins = 20,
  neighbors = 50,
  distance = gower::gower_dist
)

Value

An object of the class 'predict_diagnostics'. It's a data frame with calculated distribution of residuals.

Arguments

explainer

a model to be explained, preprocessed by the 'explain' function

new_observation

a new observation for which predictions need to be explained

variables

character - name of variables to be explained

...

other parameters

nbins

number of bins for the histogram. By default 20

neighbors

number of neighbors for histogram. By default 50.

distance

the distance function, by default the gower_dist() function.

References

Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/

Examples

# \donttest{
library("ranger")
titanic_glm_model <- ranger(survived ~ gender + age + class + fare + sibsp + parch,
                     data = titanic_imputed)
explainer_glm <- explain(titanic_glm_model,
                         data = titanic_imputed,
                         y = titanic_imputed$survived)
johny_d <- titanic_imputed[24, c("gender", "age", "class", "fare", "sibsp", "parch")]

id_johny <- predict_diagnostics(explainer_glm, johny_d, variables = NULL)
id_johny
plot(id_johny)

id_johny <- predict_diagnostics(explainer_glm, johny_d,
                       neighbors = 10,
                       variables = c("age", "fare"))
id_johny
plot(id_johny)

id_johny <- predict_diagnostics(explainer_glm,
                       johny_d,
                       neighbors = 10,
                       variables = c("class", "gender"))
id_johny
plot(id_johny)
# }