Example of global variable importance"

In vivo: Variable Importance via Oscillations

knitr::opts_chunk$set(
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Example of global variable importance

In this vignette, we present a global variable importance measure based on Partial Dependence Profiles (PDP) for the random forest regression model.

library("ggplot2")

1 Dataset

We work on Apartments dataset from DALEX package.

library("DALEX")
data(apartments)
head(apartments)

2 Random forest regression model

Now, we define a random forest regression model and use explain() function from DALEX.

library("randomForest")
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
                                      no.rooms, data = apartments)
explainer_rf <- explain(apartments_rf_model,
                        data = apartmentsTest[,2:5], y = apartmentsTest$m2.price)

3 Calculate Partial Dependence Profiles

Let see the Partial Dependence Profiles calculated with DALEX::model_profile() function. The PDP also can be calculated with DALEX::variable_profile() or ingredients::partial_dependence().

profiles <- model_profile(explainer_rf)
plot(profiles) 

4 Calculate measure of global variable importance

Now, we calculated a measure of global variable importance via oscillation based on PDP.

library("vivo")
measure <- global_variable_importance(profiles)

plot(measure)

The most important variable is surface, then no.rooms, floor, and construction.year.

5 Comparison of the importance of variables for two or more models

Let created a linear regression model and explain object.

apartments_lm_model <- lm(m2.price ~ construction.year + surface + floor +
                                      no.rooms, data = apartments)
explainer_lm <- explain(apartments_lm_model,
                        data = apartmentsTest[,2:5], y = apartmentsTest$m2.price)

We calculated Partial Dependence Profiles and measure.

profiles_lm <- model_profile(explainer_lm)

measure_lm <- global_variable_importance(profiles_lm)

plot(measure_lm, measure, type = "lines")

Now we can see the order of importance of variables by model.

vivo documentation built on Sept. 7, 2020, 5:09 p.m.