lares (version 5.1.0)

h2o_explainer: DALEX Explainer for H2O

Description

DALEX helper function to create an explainer object using a h2o trained model.

Usage

h2o_explainer(df, model, y = "tag", ignore = NULL, ...)

Arguments

df

Dataframe. Must contain all columns and predictions

model

Model object (H2O)

y

Character or Variable name. Variable's column name.

ignore

Character vector. Which columns should be ignored?

...

Additional parameters to pass to h2o_predict_model or h2o_predict_MOJO.

Value

List; explainer. Containing the model, data, y, predict_function, y_hat, residuals, class, label, model_info, residual_function, and weights.

See Also

Other Interpretability: dalex_local(), dalex_residuals(), dalex_variable()

Examples

# NOT RUN {
# You must have "DALEX" library to use this auxiliary function:
# }
# NOT RUN {
data(dft) # Titanic dataset

# TRAIN A SIMPLE MODEL
dfm <- h2o_automl(dft,
  y = "Survived",
  ignore = c("Ticket", "PassengerId", "Cabin"),
  max_models = 1
)

# EXPLAINER
explainer <- h2o_explainer(df = dfm$datasets$test, model = dfm$model, y = "Survived")
explainer$data <- na.omit(explainer$data)

# CATEGORICAL EXAMPLE
class <- dalex_variable(explainer, vars = c("Pclass", "Sex"))
class$plot

# NUMERICAL EXAMPLE
num <- dalex_variable(explainer, vars = c("Fare", "Age"))
num$plot

# LOCAL EXAMPLE
local <- dalex_local(explainer, row = 1)
# OR YOU COULD MANUALLY INPUT THE OBSERVATION
local <- dalex_local(explainer, observation = explainer$data[1, ])
local$plot

# xai2shiny's UI (needs to be installed from ModelOriented/xai2shiny)
xai2shiny(explainer, run = TRUE)
# }