predict_diagnostics: Instance Level Residual Diagnostics

In DALEX: moDel Agnostic Language for Exploration and eXplanation

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
)

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.

Value

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

References

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

Examples

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)

DALEX documentation built on Jan. 16, 2023, 1:06 a.m.