DALEXtra (version 0.2.1)

print.training_test_comparison: Print funnel_measure object

Description

Print funnel_measure object

Usage

# S3 method for training_test_comparison
print(x, ...)

Arguments

x

an object of class funnel_measure

...

other parameters

Examples

# NOT RUN {
library(DALEXtra)
titanic_train <- read.csv(system.file("extdata", "titanic_train.csv", package = "DALEXtra"))
titanic_test <- read.csv(system.file("extdata", "titanic_test.csv", package = "DALEXtra"))
h2o::h2o.init()
h2o::h2o.no_progress()
titanic_h2o <- h2o::as.h2o(titanic_train)
titanic_h2o["survived"] <- h2o::as.factor(titanic_h2o["survived"])
titanic_test_h2o <- h2o::as.h2o(titanic_test)
model <- h2o::h2o.gbm(
  training_frame = titanic_h2o,
  y = "survived",
  distribution = "bernoulli",
  ntrees = 500,
  max_depth = 4,
  min_rows =  12,
  learn_rate = 0.001
)
explainer_h2o <- explain_h2o(model, titanic_test[,1:17], titanic_test[,18])

explainer_scikit <- explain_scikitlearn(system.file("extdata",
                                                    "scikitlearn.pkl",
                                                    package = "DALEXtra"),
                                        yml = system.file("extdata",
                                                          "testing_environment.yml",
                                                          package = "DALEXtra"),
                                        data = titanic_test[,1:17],
                                        y = titanic_test$survived)

library("mlr")
task <- mlr::makeClassifTask(
  id = "R",
  data = titanic_train,
  target = "survived"
)
learner <- mlr::makeLearner(
  "classif.gbm",
  par.vals = list(
    distribution = "bernoulli",
    n.trees = 500,
    interaction.depth = 4,
    n.minobsinnode = 12,
    shrinkage = 0.001,
    bag.fraction = 0.5,
    train.fraction = 1
  ),
  predict.type = "prob"
)
gbm <- mlr::train(learner, task)
explainer_mlr <- explain_mlr(gbm, titanic_test[,1:17], titanic_test[,18])

data <- training_test_comparison(explainer_scikit, list(explainer_h2o, explainer_mlr),
                                 training_data = titanic_train[,-18],
                                 training_y = titanic_train[,18])
print(data)
# }