SHAP values for H2O Models

SHAP (SHapley Additive exPlanations) by Lundberg and Lee (2016) is a method to explain individual predictions. SHAP is based on the game theoretically optimal Shapley Values. Calculate SHAP values for h2o models in which each row is an observation and each column a feature. Use plot method to visualize features importance and distributions.

Usage

h2o_shap(model, test = "auto", scores = "auto", y = "y", ...)

# S3 method for class 'h2o_shap'
plot(x, relevant = TRUE, top = 15, quiet = FALSE, ...)

Arguments

model

h2o_automl object or h2o model.

test

String or Dataframe. Leave "auto" to use h2o_automl's test dataset or pass a valid dataframe.

scores

Numeric vector. If test != "auto", you must provide predicted values

y

Character. If test != "auto", you must provide y variable's name

...

Additional argument for predict_contributions.H2OModel

x

h2o_shap object

relevant

Boolean. Keep only relevant non-trivial (>0) features

top

Integer. Plot only top n values (as in importance)

quiet

Boolean. Print messages?

Value

H2OFrame with shap values for every observation and feature.

See also

Other SHAP: shap_var()

Examples

if (FALSE) { # \dontrun{
# Train a h2o_automl model
model <- h2o_automl(dft, Survived,
  max_models = 1, target = TRUE,
  ignore = c("Ticket", "Cabin", "PassengerId"),
  quiet = TRUE
)

# Calculate SHAP values
SHAP_values <- h2o_shap(model)
# Equivalent to:
# SHAP_values <- h2o_shap(
#  model = model$model,
#  test = model$datasets$test,
#  scores = model$scores_test$scores)

# Check SHAP results
head(SHAP_values)

# You must have "ggbeeswarm" library to use this auxiliary function:
# Plot SHAP values (feature importance)
plot(SHAP_values)

# Plot some of the variables (categorical)
shap_var(SHAP_values, Pclass)

# Plot some of the variables (numerical)
shap_var(SHAP_values, Fare)
} # }