plot.variable_importance_explainer: Plots Global Model Explanations (Variable Importance)

Description

Function plot.variable_dropout_explainer plots dropouts for variables used in the model. It uses output from variable_dropout 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 mdoels.

Usage

# S3 method for variable_importance_explainer
plot(x, ..., max_vars = 10,
  bar_width = 10, show_baseline = FALSE, desc_sorting = TRUE)

Arguments

a variable dropout exlainer produced with the 'variable_dropout' function

...

other explainers that shall be plotted together

max_vars

maximum number of variables that shall be presented for for each model

bar_width

width of bars. By default 10

show_baseline

logical. Should the baseline be included?

desc_sorting

logical. Should the bars be sorted descending? By default TRUE

Value

a ggplot2 object

Examples

Run this code

# NOT RUN {
 
# }
# NOT RUN {
library("breakDown")
library("randomForest")
HR_rf_model <- randomForest(status == "fired"~., data = HR, ntree = 100)
explainer_rf  <- explain(HR_rf_model, data = HR, y = HR$status == "fired")
vd_rf <- variable_importance(explainer_rf, type = "raw")
head(vd_rf)
plot(vd_rf)

HR_glm_model <- glm(status == "fired"~., data = HR, family = "binomial")
explainer_glm <- explain(HR_glm_model, data = HR, y = HR$status == "fired")
logit <- function(x) exp(x)/(1+exp(x))
vd_glm <- variable_importance(explainer_glm, type = "raw",
                        loss_function = function(observed, predicted)
                                   sum((observed - logit(predicted))^2))
head(vd_glm)
plot(vd_glm)

library("xgboost")
model_martix_train <- model.matrix(status == "fired"~.-1, HR)
data_train <- xgb.DMatrix(model_martix_train, label = HR$status == "fired")
param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2,
              objective = "binary:logistic", eval_metric = "auc")
HR_xgb_model <- xgb.train(param, data_train, nrounds = 50)
explainer_xgb <- explain(HR_xgb_model, data = model_martix_train,
                                    y = HR$status == "fired", label = "xgboost")
vd_xgb <- variable_importance(explainer_xgb, type = "raw")
head(vd_xgb)
plot(vd_xgb)

plot(vd_rf, vd_glm, vd_xgb, bar_width = 4)

# NOTE:
# if you like to have all importances hooked to 0, you can do this as well
vd_rf <- variable_importance(explainer_rf, type = "difference")
vd_glm <- variable_importance(explainer_glm, type = "difference",
                        loss_function = function(observed, predicted)
                                   sum((observed - logit(predicted))^2))
vd_xgb <- variable_importance(explainer_xgb, type = "difference")
plot(vd_rf, vd_glm, vd_xgb, bar_width = 4)
 
# }
# NOT RUN {
# }

Run the code above in your browser using DataCamp Workspace