The modelStudio() function computes various (instance and dataset level) model explanations and produces an interactive, customisable dashboard. It consists of multiple panels for plots with their short descriptions. Easily save and share the HTML dashboard with others. Tools for model exploration unite with tools for EDA (Exploratory Data Analysis) to give a broad overview of the model behavior.

Let’s use HR dataset to explore modelStudio parameters:

train <- DALEX::HR
train$fired <- as.factor(ifelse(train$status == "fired", 1, 0))
train$status <- NULL

DALEX::HR dataset
gender age hours evaluation salary fired
male 32.58 41.89 3 1 1
female 41.21 36.34 2 5 1
male 37.71 36.82 3 0 1
female 30.06 38.96 3 2 1
male 21.10 62.15 5 3 0
male 40.12 69.54 2 0 1

Prepare HR_test data and a ranger model for the explainer:

# fit a ranger model
model <- ranger(fired ~., data = train, probability = TRUE)

# prepare validation dataset
test <- DALEX::HR_test[1:1000,]
test$fired <- ifelse(test$status == "fired", 1, 0)
test$status <- NULL

# create an explainer for the model
explainer <- DALEX::explain(model,
                            data = test,
                            y = test$fired)

# start modelStudio

modelStudio parameters

instance explanations

Pass data points to the new_observation parameter for instance explanations such as Break Down, Shapley Values and Ceteris Paribus Profiles. Use new_observation_y to show their true labels.

new_observation <- test[1:3,]
rownames(new_observation) <- c("John Snow", "Arya Stark", "Samwell Tarly")
true_labels <- test[1:3,]$fired

            new_observation = new_observation,
            new_observation_y  = true_labels)

grid size

Achieve bigger or smaller modelStudio grid with facet_dim parameter.

# small dashboard with 2 panels
            facet_dim = c(1,2))

# large dashboard with 9 panels
            facet_dim = c(3,3))


Manipulate time parameter to set animation length. Value 0 will make them invisible.

# slow down animations
            time = 1000)

# turn off animations
            time = 0)

more calculations means more time

Decrease N and B parameters to lower the computation time or increase them to get more accurate empirical results.

# faster, less precise
            N = 200, B = 5)

# slower, more precise
            N = 500, B = 15)

no EDA mode

Don’t compute the EDA plots if they are not needed. Set the eda parameter to FALSE.

            eda = FALSE)

progress bar

Hide computation progress bar messages with show_info parameter.

            show_info = FALSE)

viewer or browser?

Change viewer parameter to set where to display modelStudio. Best described in r2d3 documentation.

            viewer = "browser")

parallel computation

Speed up modelStudio computation by setting parallel parameter to TRUE. It uses parallelMap package to calculate local explainers faster. It is really useful when using modelStudio with complicated models, vast datasets or many observations are being processed.

All options can be set outside of the function call. How to use parallelMap.

# set up the cluster
  parallelMap.default.mode        = "socket",
  parallelMap.default.cpus        = 4,   = FALSE

# calculations of local explanations will be distributed into 4 cores
            new_observation = test[1:16,],
            parallel = TRUE)

additional options

Customize some of the modelStudio looks by overwriting default options returned by the ms_options() function. Full list of options.

# set additional graphical parameters
new_options <- ms_options(
  show_subtitle = TRUE,
  bd_subtitle = "Hello World",
  line_size = 5,
  point_size = 9,
  line_color = "pink",
  point_color = "purple",
  bd_positive_color = "yellow",
  bd_negative_color = "orange"

            options = new_options)

All visual options can be changed after the calculations using ms_update_options().

old_ms <- modelStudio(explainer)

# update the options
new_ms <- ms_update_options(old_ms,
                            time = 0,
                            facet_dim = c(1,2),
                            margin_left = 150)

update observations

Use ms_update_observations() to add more observations with their local explanations to the modelStudio.

old_ms <- modelStudio(explainer)

# add new observations
plus_ms <- ms_update_observations(old_ms,
                                  new_observation = test[101:102,])

# overwrite old observations
new_ms <- ms_update_observations(old_ms,
                                 new_observation = test[103:104,],
                                 overwrite = TRUE)


Use explain_*() functions from the DALEXtra package to explain various models.

Bellow basic example of making modelStudio for a mlr model using explain_mlr().


# fit a model
task <- makeClassifTask(id = "task", data = train, target = "fired")
learner <- makeLearner("classif.ranger", predict.type = "prob")
model <- train(learner, task)

# create an explainer for the model
explainer_mlr <- explain_mlr(model,
                             data = test,
                             y = test$fired,
                             label = "mlr")

# make a studio for the model