plot.feature_importance_explainer: Plots Feature Importance

In ingredients: Effects and Importances of Model Ingredients

Plots Feature Importance

Description

This function plots variable importance calculated as changes in the loss function after variable drops. It uses output from feature_importance function that corresponds to permutation based measure of variable importance. Variables are sorted in the same order in all panels. The order depends on the average drop out loss. In different panels variable contributions may not look like sorted if variable importance is different in different in different models.

Usage

## S3 method for class 'feature_importance_explainer'
plot(
  x,
  ...,
  max_vars = NULL,
  show_boxplots = TRUE,
  bar_width = 10,
  desc_sorting = TRUE,
  title = "Feature Importance",
  subtitle = NULL
)

Arguments

x	a feature importance explainer produced with the feature_importance() function
...	other explainers that shall be plotted together
max_vars	maximum number of variables that shall be presented for for each model. By default NULL what means all variables
show_boxplots	logical if TRUE (default) boxplot will be plotted to show permutation data.
bar_width	width of bars. By default 10
desc_sorting	logical. Should the bars be sorted descending? By default TRUE
title	the plot's title, by default 'Feature Importance'
subtitle	the plot's subtitle. By default - NULL, which means the subtitle will be 'created for the XXX model', where XXX is the label of explainer(s)

Details

Find more details in the Feature Importance Chapter.

Value

a ggplot2 object

References

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

Examples

library("DALEX")
library("ingredients")

model_titanic_glm <- glm(survived ~ gender + age + fare,
                         data = titanic_imputed, family = "binomial")

explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_imputed[,-8],
                               y = titanic_imputed[,8])

fi_rf <- feature_importance(explain_titanic_glm, B = 1)
plot(fi_rf)


library("ranger")
model_titanic_rf <- ranger(survived ~., data = titanic_imputed, probability = TRUE)

explain_titanic_rf <- explain(model_titanic_rf,
                              data = titanic_imputed[,-8],
                              y = titanic_imputed[,8],
                              label = "ranger forest",
                              verbose = FALSE)

fi_rf <- feature_importance(explain_titanic_rf)
plot(fi_rf)

HR_rf_model <- ranger(status~., data = HR, probability = TRUE)

explainer_rf  <- explain(HR_rf_model, data = HR, y = HR$status,
                         verbose = FALSE, precalculate = FALSE)

fi_rf <- feature_importance(explainer_rf, type = "raw", max_vars = 3,
                            loss_function = DALEX::loss_cross_entropy)
head(fi_rf)
plot(fi_rf)

HR_glm_model <- glm(status == "fired"~., data = HR, family = "binomial")
explainer_glm <- explain(HR_glm_model, data = HR, y = as.numeric(HR$status == "fired"))

fi_glm <- feature_importance(explainer_glm, type = "raw",
                             loss_function = DALEX::loss_root_mean_square)
head(fi_glm)
plot(fi_glm)

ingredients documentation built on Jan. 15, 2023, 5:09 p.m.