ceterisParibus (version 0.4.2)

plot.local_fit_explainer: Local Fit Plots / Wangkardu Explanations

Description

Function 'plot.local_fit_explainer' plots Local Fit Plots for a single prediction / observation.

Usage

# S3 method for local_fit_explainer
plot(x, ..., plot_residuals = TRUE, palette = "default")

Value

a ggplot2 object

Arguments

x

a local fir explainer produced with the 'local_fit' function

...

other explainers that shall be plotted together

plot_residuals

if TRUE (default) then residuals are plotted as red/blue bars

palette

color palette. Currently the choice is limited to 'wangkardu' and 'default'

Examples

library("DALEX")
 if (FALSE) {
library("randomForest")
set.seed(59)

apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
      no.rooms + district, data = apartments)

explainer_rf <- explain(apartments_rf_model,
      data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)

new_apartment <- apartmentsTest[1, ]
new_apartment

cr_rf <- local_fit(explainer_rf, observation = new_apartment,
    select_points = 0.002, selected_variable = "surface")
plot(cr_rf, plot_residuals = FALSE)
plot(cr_rf)

cr_rf <- local_fit(explainer_rf, observation = new_apartment,
          select_points = 0.002, selected_variable = "surface")
plot(cr_rf, plot_residuals = FALSE, palette = "wangkardu")
plot(cr_rf, palette = "wangkardu")

new_apartment <- apartmentsTest[10, ]
cr_rf <- local_fit(explainer_rf, observation = new_apartment,
          select_points = 0.002, selected_variable = "surface")
plot(cr_rf, plot_residuals = FALSE)
plot(cr_rf)

new_apartment <- apartmentsTest[302, ]
cr_rf <- local_fit(explainer_rf, observation = new_apartment,
          select_points = 0.002, selected_variable = "surface")
plot(cr_rf, plot_residuals = FALSE)
plot(cr_rf)

new_apartment <- apartmentsTest[720, ]
cr_rf <- local_fit(explainer_rf, observation = new_apartment,
         select_points = 0.002, selected_variable = "surface")
plot(cr_rf, plot_residuals = FALSE)
plot(cr_rf)
}