Let’s compare three models: GLM and GBMs with 100 and 500 trees. For each we create explainer from DALEX package.
library(gbm) library(DALEX) library(dplyr) model_gbm100 <- gbm(m2.price ~ ., data = apartments, n.trees = 100) expl_gbm100 <- explain( model_gbm100, data = apartments, y = apartments$m2.price, label = "gbm [100 trees]" ) model_gbm500 <- gbm(m2.price ~ ., data = apartments, n.trees = 500) expl_gbm500 <- explain( model_gbm500, data = apartments, y = apartments$m2.price, label = "gbm [500 trees]" ) model_glm <- glm(m2.price ~ ., data = apartments) expl_glm <- explain(model_glm, data = apartments, y = apartments$m2.price)
It is not necessary to use dplyr’s pipes, but it’s designed to use pipes.
Oservations’ names are taken from rownames. For example let’s put district and surface as observation name.
apartments2 <- apartments rownames(apartments2) <- paste0( 1:nrow(apartments2), ". ", apartments2$surface, "m2 ", apartments2$district ) create_arena(live = TRUE) %>% push_model(expl_glm) %>% push_observations(apartments2) %>% run_server()